diff --git "a/1tAzT4oBgHgl3EQfRfvj/content/tmp_files/load_file.txt" "b/1tAzT4oBgHgl3EQfRfvj/content/tmp_files/load_file.txt" new file mode 100644--- /dev/null +++ "b/1tAzT4oBgHgl3EQfRfvj/content/tmp_files/load_file.txt" @@ -0,0 +1,838 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf,len=837 +page_content='Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence Han Fang1, Jiyi Zhang 1, Yupeng Qiu 1, Ke Xu 2, Chengfang Fang 2, Ee-Chien Chang 1* 1 National University of Singapore 2 Huawei International fanghan@nus.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='sg, jiyizhang@u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='nus.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='edu, qiu yupeng@u.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='nus.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='edu, changec@comp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='nus.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='sg Abstract Deep neural networks are vulnerable to adversarial attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In this paper, we take the role of investigators who want to trace the attack and identify the source, that is, the particular model which the adversarial examples are generated from.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Techniques derived would aid forensic investigation of at- tack incidents and serve as deterrence to potential attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We consider the buyers-seller setting where a machine learning model is to be distributed to various buyers and each buyer receives a slightly different copy with same functionality.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' A malicious buyer generates adversarial examples from a par- ticular copy Mi and uses them to attack other copies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' From these adversarial examples, the investigator wants to iden- tify the source Mi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' To address this problem, we propose a two-stage separate-and-trace framework.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The model separa- tion stage generates multiple copies of a model for a same classification task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' This process injects unique characteristics into each copy so that adversarial examples generated have distinct and traceable features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We give a parallel structure which embeds a “tracer” in each copy, and a noise-sensitive training loss to achieve this goal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The tracing stage takes in adversarial examples and a few candidate models, and iden- tifies the likely source.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Based on the unique features induced by the noise-sensitive loss function, we could effectively trace the potential adversarial copy by considering the output logits from each tracer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Empirical results show that it is possible to trace the origin of the adversarial example and the mechanism can be applied to a wide range of architectures and datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 1 Introduction Deep learning models are vulnerable to adversarial attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' By introducing specific perturbations on input samples, the network model could be misled to give wrong predictions even when the perturbed sample looks visually close to the clean image (Szegedy et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2014;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Goodfellow, Shlens, and Szegedy 2014;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Moosavi-Dezfooli, Fawzi, and Frossard 2016;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Carlini and Wagner 2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' There are many existing works on defending against such attacks (Kurakin, Good- fellow, and Bengio 2016;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Meng and Chen 2017;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Gu and Rigazio 2014;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Hinton, Vinyals, and Dean 2015).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Unfortu- nately, although current defenses could mitigate the attack to some extent, the threat is still far from being completely eliminated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In this paper, we look into the forensic aspect: from the adversarial examples, can we determine which Corresponding Authors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Figure 1: Buyers-seller setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The seller has multiple mod- els Mi, i ∈ [1, m] that are to be distributed to different buyers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' A malicious buyer batt attempts to attack the vic- tim buyer bvic by generating the adversarial examples with his own model Matt.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' model the adversarial examples were derived from?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Tech- niques derived could aid forensic investigation of attack in- cidents and provide deterrence to future attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We consider a buyers-seller setting (Zhang, Tann, and Chang 2021), which is similar to the buyers-seller setting in digital rights protection (Memon and Wong 2001).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Buyers-seller Setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Under this setting, the seller S dis- tributes m classification models Mi, i ∈ [1, m] to different buyers bi’s as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' These models are trained for a same classification task using a same training dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The models are made accessible to the buyer as black boxes, for instance, the models could be embedded in hardware such as FPGA and ASIC, or are provided in a Machine Learning as a Service (MLaaS) platform.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Hence, the buyer only has black- box access, which means that he can only query the model for the hard label.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In addition, we assume that the buyers do not know the training datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The seller has full knowledge and thus has white-box access to all the distributed models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Attack and Traceability.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' A malicious buyer wants to at- tack other victim buyers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The malicious buyer does not have direct access to other models and thus generates the exam- ples from its own model and then deploys the found exam- ples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For example, the malicious buyer might generate an ad- versarial example of a road sign using its self-driving vehi- cle, and then physically defaces the road sign to trick passing vehicles.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Now, as forensic investigators who have obtained the defaced road sign, we want to understand how the ad- versarial example is generated and trace the models used in generating the example.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='01218v1 [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='CR] 31 Dec 2022 M1 M2 M1 M3 Matt Attacking 20 Generating Adversarial ExamplesProposed Framework.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' There are two stages in our solu- tion: model separation and origin tracing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' During the model separation stage, given a classification task, we want to gen- erate multiple models that have high accuracy on the clas- sification task and yet are sufficiently different for tracing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In other words, we want to proactively enhance differences among the models in order to facilitate tracing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' To achieve that, we propose a parallel network structure that pairs a unique tracer with the original classification model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The role of the tracer is to modify the output, so as to induce the at- tacker to adversarial examples with unique features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We give a noise-sensitive training loss for the tracer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' During the tracing stage, given m different classification models Mi, i ∈ [1, m] and the found adversarial example, we want to determine which model is most likely used in generating the adversarial examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' This is achieved by ex- ploiting the different tracers that are earlier embedded into the parallel models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Our proposed method compares the out- put logits (the output of the network before softmax) of those tracers to identify the source.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In a certain sense, traceability is similar to neural network watermarking and can be viewed as a stronger form of water- marking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Neural network watermarking schemes (Boenisch 2020) attempt to generate multiple models so that an investi- gator can trace the source of a modified copy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In traceability, the investigator can trace the source based on the generated adversarial examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Contributions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We point out a new aspect in defending against adver- sarial attacks, that is, tracing the origin of adversarial samples among multiple classifiers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Techniques derived would aid forensic investigation of attack incidents and provide deterrence to future attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We propose a framework to achieve traceability in the buyers-seller setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The framework consists of two stages: a model separation stage, and a tracing stage.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The model separation stage generates multiple “well- separated” models and this is achieved by a parallel network structure that pairs a tracer with the classifier.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The tracing mechanism exploits the characteristics of the paired tracers to decide the origin of the given adversarial examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We investigate the effectiveness of the separation and the subsequent tracing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Experimental studies show that the proposed mechanism can effectively trace to the source.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For example, the tracing accuracy achieves more than 97% when applying to “ResNet18-CIFAR10” task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We also observe a clear separation of the source tracer’s log- its distribution, from the non-source’s logits distribution (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 5a-5c).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2 Related Work In this paper, we adopt black-box settings where the adver- sary can only query the model and get the hard label (final decision) of the output.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Many existing attacks assume white- box settings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Attack such as FGSM (Goodfellow, Shlens, and Szegedy 2014), PGD (Kurakin, Goodfellow, and Bengio 2016), JSMA (Papernot et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2016), DeepFool (Moosavi- Dezfooli, Fawzi, and Frossard 2016), CW (Carlini and Wag- ner 2017) and EAD (Chen et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2018) usually directly rely on the gradient information provided by the victim model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' As the detailed information of the model is hidden in black- box settings, black-box attacks are often considered more difficult and there are fewer works.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Chen et.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' introduced a black-box attack called Zeroth Order Optimization (ZOO) (Chen et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' ZOO can approximate the gradients of the objective function with finite-difference numerical esti- mates by only querying the network model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Thus the ap- proximated gradient is utilized to generate the adversarial examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Guo et.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' proposed a simple black-box adver- sarial attack called “SimBA” (Guo et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2019) to generate adversarial examples with a set of orthogonal vectors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' By testing the output logits with the added chosen vector, the optimization direction can be effectively found.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Brendel et.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' developed a decision-based adversarial attack which is known as “Boundary attack” (Brendel, Rauber, and Bethge 2018), it worked by iteratively perturbing another initial im- age that belongs to a different label toward the decision boundaries between the original label and the adjacent la- bel.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' By querying the model with enough perturbed images, the boundary as well as the perturbation can be found thus generating the adversarial examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Chen et.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' proposed another decision based attack named hop-skip-jump attack (HSJA) (Chen, Jordan, and Wainwright 2020) recently.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' By only utilizing the binary information at the decision bound- ary and the Monte-Carlo estimation, the gradient direction of the network can be found so as to realize the adversarial ex- amples generation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Based on (Chen, Jordan, and Wainwright 2020), Li et.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2020) proposed a query-efficient boundary-based black-box attack named QEBA which es- timate the gradient of the boundary in several transformed space and effectively reduce the query numbers in gener- ating the adversarial examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Maho et.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (Maho, Furon, and Le Merrer 2021) proposed a surrogate-free black-box attack which do not estimate the gradient but searching the boundary based on polar coordinates, compared with (Chen, Jordan, and Wainwright 2020) and (Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2020), (Maho, Furon, and Le Merrer 2021) achieves less distortion with less query numbers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 3 Proposed Framework 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 Main Idea We design a framework that contains two stages: model sep- aration and origin tracing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' During the model separation stage, we want to generate multiple models which are sufficiently different under ad- versarial attack while remaining highly accurate on the clas- sification task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Our main idea is a parallel network structure which pairs a unique tracer with the original classifier.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The specific structure will be illustrated in Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' As for origin tracing, we exploit unique characteristics of different tracers in the parallel structure, which can be ob- served in the tracers’ logits.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Hence, our tracing process is conducted by feeding the adversarial examples into the trac- ers and analyzing their output.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Figure 2: The framework of the proposed method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The left part of the framework indicates the separation process of the seller’s distributed models Mi, i ∈ [1, m].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The right part of the framework illustrates the origin tracing process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The whole framework of the proposed scheme is shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' As illustrated in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2, each distributed model Mi consists of a tracer Ti and the original classification model C, and the tracer is trained with a proposed noise-sensitive loss LNS.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' During the tracing stage, the adversarial examples are fed into each Ti and the outputs are analyzed to identify the origin.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2 Model Separation We design a parallel network structure to generate the dis- tributed models Mi, i ∈ [1, m], which contains a tracer model Ti and a main model C, as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 3a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Ti is used for injecting unique features and setting traps for the attacker.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' C is the network trained for the original task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The final results are determined by both C and Ti with a weight parameter α.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In each distributed model, C is fixed and only Ti is different.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The specific structure of Ti is shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 3b, it is linearly cascaded with one “SingleConv” block (Conv-BN- ReLU), two “Res-block” (He et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2016), one “Conv” block, one full connection block and one “Tanh” activation layer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The training process of Ti can be described as: 1) Given the training dataset1 and tracer Ti, we first ini- tialize Ti with random parameters.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2) For each training epoch, we add random noise No 2 on the input image x to generate the noised image xNo.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 3) Then we feed both x and xNo into Ti and get the out- puts Ox and OxNo.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We attempt to make Ti sensitive to noise, so Ox and OxNo should be as different as possible.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The loss function of Ti can be written as: LNS = |Ox ◦ OxNo| ∥Ox∥2∥OxNo∥2 = |Ti(θTi, x) ◦ Ti(θTi, xNo)| ∥Ti(θTi, x)∥2∥Ti(θTi, xNo)∥2 (1) 1The training dataset for Ti only contains 1000 random sampled images from the dataset of the original classification task 2No follows a uniform distribution over [0, 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='03) where ◦ represents the Hadamard product.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' θTi indicates the parameters of Ti.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Each distributed Ti for different buyers is generated by randomly initializing and then training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We believed the ran- domness in initialization is enough to guarantee the differ- ence from different Ti.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' It should be noted that when pro- ducing a new distributed copy, we only have to train one new tracer without setting more constraints on former trac- ers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' So such a separation method can be applied to multiple distributed models independently.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' As for C, it is trained in a normal way which utilizes the whole training dataset and cross-entropy loss.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For the main classification task, C only has to be trained once.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Besides, the training of C is independent of the training of Ti.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' After train- ing C, we could get a high accuracy classification model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The final distributed model Mi is parallel combined with C and Ti.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The specific workflow of Mi can be described as: For input image x, Ti and C both receive the same x and output two different vectors OTi and OC respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' OTi and OC have the same size and will be further added in a weighted way to generate the final outputs OF , as shown in Eq.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' OF = OC + α × OTi (2) where α is the weight parameter.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' It is worth noting that for the output of C, we use the normalization form of it, which can be formulated as: OC = C(x) − min(C(x)) max(C(x)) − min(C(x)) (3) where x indicates the input image, max and min indicate the maximum value and minimum value respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' By utilizing the aforementioned model separation method, two properties are well satisfied: (I) The attack could be tricked to focus more on Ti than C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Since after the training, Ti will be sensitive to random noise.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Therefore, the output of Ti is easy to be changed by adding noise.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Com- pared with C, the boundary of Ti is more likely to be esti- mated and Ti is more likely to be attacked.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=" Thus, the attacker Model Separation Stage Origin Tracing Stage Attacker's Model !" metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=" Main Model Initialized Tracer Model Distributed Tracer (source model) Ms c T1 T2 Tini Tini Tini 2 m Adversarial Trace's Outputs Obtaining Model Combination Tracer Model Training Examples Outputs OTi(x) Outputs Outputs Outputs Adversarial Tracer Image Example OT1 OT2 OTs OTm Ti X Tracer Noise-sensitive Loss L'is." metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Image Main Ti Noised x Image c Attacked Model Tracing Identified Tracer : xNo Outputs OTi(xNo) Mi Ts Outputs Tracing mechanism OTm no Generated Models OTs arg max 0 ho true Identified Model : att: attacked label M1 M2 M3 M4 Ms Mm true: true label Ms(a) Parallel network structure.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (b) The architecture of tracer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (c) Differences in logits.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Figure 3: The specific network design in model separation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' will fall into the trap of Ti and the generated adversarial per- turbations will bring the feature of the source Ti.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (II) Based on random initialization, each distributed Ti will correspond to different adversarial perturbations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' This property helps us in tracing, since the source Ts which generates adversarial examples will output unique responses compared with other Ti, i ̸= s when feeding the generated adversarial examples, as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 3c.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='3 Tracing the Origin The tracing process is conducted by two related compo- nents: The first component keeps white-box copies for each of the m distributed copies 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' This component allows us to obtain the output logits of each tracer on an input x.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The second component is an output logits-based mech- anism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' It gives a decision on which copy i is the most likely one to generate the adversarial example.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The specific tracing process can be described as follows: 1) Given an appeared adversarial examples denoted as xatt, we feed the adversarial example into all Ti, i ∈ [1, m] and obtain the output logits of them, noted as OTi, i ∈ [1, m].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2) Then we extract two values that are corresponding to the attacked label and true label in each OTi, denoted as OTi att and OTi true respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 4 3) The source model can be determined by: s = arg max i,i∈[1,m] (OTi att − OTi true) (4) To simplify the description, we denote the difference of out- put logits (OTi att−OTi true) as DOL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The tracer corresponded to the largest DOL is regarded as the source model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The reason is as follows: Since the perturbation are highly related to Ti, when feed- ing the same adversarial example, the outputs of Ti and Tj 3This setting is reasonable because when an adversarial attack appeared, the model seller who has all the details of the distributed network takes responsible to trace the attacker.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 4Attacked label can be easily determined by the output logits and the true label can be tagged by the model owner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' If this sample cannot be accurately tagged by the owner, then this sample is not regarded as an adversarial example.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (i ̸= j) will be certainly different.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For source model Ts where the adversarial examples are generated from, OTs is likely to render a large value on the adversarial label and a small value on the ground-truth label.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Since the weight of OTs in the final OFs is small, so in order to achieve ad- versarial attack, OTs will be modified as much as possible.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Thus DOL of Ts should be large.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' But for victim model Tv, the DOL will be small.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Therefore, according to the value of DOL, we can trace the origin of the adversarial example.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 4 Experimental Results 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 Implementation Details In order to show the effectiveness of the proposed frame- work, we perform the experiments on two network architec- ture (ResNet18 (He et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2016) and VGG16 (Simonyan and Zisserman 2014)) with two small image datasets (CIFAR10 (Krizhevsky, Hinton et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2009) of 10 classes and GTSRB (Houben et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2013) of 43 classes) and two deeper network architecture (ResNet50 and VGG19) with one big image dataset (mini-ImageNet (Ravi and Larochelle 2016) of 100 classes).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The main classifier C in experiments is trained for 200 epochs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' All the model training is implemented by Py- Torch and executed on NVIDIA RTX 2080ti.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For gradient descent, Adam (Kingma and Ba 2015) with learning rate of 1e-4 is applied as the optimization method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2 The Classification Accuracy of The Proposed Architecture The most influenced parameter for the classification accu- racy is the weight parameter α.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' α determines the partici- pation ratio of Ti in final outputs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' To investigate the influ- ence of α, we change the value of α from 0 (baseline) to 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2 and record the corresponding classification accuracy of each task, the results are shown in Table 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' It can be seen from Table 1 that for CIFAR10 and GTSRB, the growth of α will seldom decrease the accuracy of the classification task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Compared with the baseline (α = 0), the small value of α will keep the accuracy at the same level as the baseline.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' But for mini-ImageNet, the accuracy decreases more as α increases, we believe it is due to the complexity of the classification task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' But even though, the decrease rate is still within 3% when α is not larger than 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Tracer Model Ti Image Main Model x cTracer Model T;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='Tv Ti Ti c c c cα CIFAR10 GTSRB Mini-ImageNet ResNet18 VGG16 ResNet18 VGG16 ResNet50 VGG19 0 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='30% 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='68% 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='19% 97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='59% 73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='12% 75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='79% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='24% 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='64% 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='14% 97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='52% 72.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='32% 75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='04% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='24% 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='63% 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='07% 97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='36% 71.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='88% 74.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='96% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='07% 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='63% 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='72% 96.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='84% 70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='50% 73.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='75% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='95% 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='57% 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='09% 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='52% 68.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='14% 71.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='75% Table 1: The classification accuracy with different α.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='3 Traceability of different black-box attack It should be noted that the change of α will not only influ- ence the accuracy but also affect the process of black-box adversarial attack.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Therefore, in order to explore the influ- ence of α, the following experiments will be conducted with α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05, 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Setup and Code.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' To verify the traceability of the pro- posed mechanism, we conduct experiments on two dis- tributed models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We set one model as the source model Ms to perform the adversarial attack and set the other model as the victim model Mv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The goal is to test whether the proposed scheme can effectively trace the source model from the generated adversarial examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The black-box at- tack we choose is Boundary (Brendel, Rauber, and Bethge 2018), HSJA (Chen, Jordan, and Wainwright 2020), QEBA (Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2020) and SurFree (Maho, Furon, and Le Merrer 2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For Boundary (Brendel, Rauber, and Bethge 2018) and HSJA (Chen, Jordan, and Wainwright 2020), we use Ad- versarial Robustness Toolbox (ART) (Nicolae et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2018) platform to conduct the experiments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For QEBA (Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2020) and SurFree (Maho, Furon, and Le Merrer 2021), we pull implementations from their respective GitHub reposito- ries 5 6 with default parameters.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For each α, each network architecture, each dataset and each attack, we generate 1000 successful attacked adversarial examples of Ms and con- duct the tracing experiment.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Evaluation Metrics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Traceability is evaluated by tracing accuracy, which is calculated by: Acc = Ncorrect NAll (5) where Ncorrect indicates the number of correct-tracing sam- ples and NAll indicates the total number of samples, which is set as 1000 in the experiments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The tracing performance of different attacks with different settings is shown in Table 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' It can be seen that when apply- ing ResNet-based architecture as the backbone of C, the trac- ing accuracy is higher than 90%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Especially for α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15, most of the tracing accuracy is higher than 96%, which indi- cates the effectiveness of the proposed mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Besides, for a different level of classification task and different attack- ing methods, the tracing accuracy can stay at a high level, which shows the great adaptability of the proposed scheme.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The influence of α.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We can see from Table 2 that the trac- ing accuracy increases with the increase of α.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We conclude 5QEBA:https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='com/AI-secure/QEBA 6SurFree:https://github.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='com/t-maho/SurFree the reason as: α determines the participation rate of tracer Ti in final output logits, the larger α will make the final de- cision boundary rely more on T .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Therefore, when α gets larger, making DOL of T larger would be a better choice to realize the adversarial attack.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The bigger DOL of T will cer- tainly lead to better tracing performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' To verify the cor- rectness of the explanation, we show the distribution of DOL for task “ResNet18-CIFAR10” with different attacks in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We first generate 1000 adversarial examples of model Mi for each α (0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05,0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1,0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15) with Boundary, HSJA, QEBA and SurFree attack, then we record the DOLs of Ti.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The distri- bution of DOLs are shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (a) The results of Boundary.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (b) The results of HSJA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (c) The results of QEBA.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (d) The results of SurFree.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Figure 4: The distributions of output differences with differ- ent black-box attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' It can be seen that compared with α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 and α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1, the DOL of α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 concentrate more on larger values, which indicates that the larger α will result to larger DOL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The influence of network architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The tracing re- sults vary with different networks and different datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' With the same dataset, the tracing accuracy of ResNet18 will be higher than that of VGG16.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We attribute the reason to the complexity of the model architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' According to (Su et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2018), compared with ResNet, the structure of VGG is less robust, so VGG-based C might be easier to be adversar- ial attacked.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Therefore, once C is attacked, there is a certain probability that Ti is not attacked as we expected, so DOL of Ti will not produce the expected features for tracing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Fortu- nately, the network architecture can be designed by us, so in practice, choosing a robust architecture would be better for tracing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The influence of classification task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In our experiments, we test the classification task with different classes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' It can be seen that with the increase of classification task complex- ity, traceability performance decreases slightly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' But in most cases, when α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15, the traceability ability can still reach more than 90%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The influence of black-box attack.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The mechanism of the black-box attack greatly influences the tracing perfor- mance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For Boundary attack(Brendel, Rauber, and Bethge 600 α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 500 α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 umbers of sampl α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 400 300 200 100 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='7 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='6 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='9 Outout crferences800 α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 0 sam 600 α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 of 400 mbers 200 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='5 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='8 Outout dirference800 α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 Jumbers of samp 600 α= 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 400 200 3 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='9 Outout dirferences800 α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 Q Jumbers of samr 600 α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 400 200 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='5 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='8 2 Output differencesAttack Boundary HSJA QEBA SurFree alpha 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 CIFAR10 ResNet18 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1% 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='9 % 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2 % 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2% 99.' metadata={'source': 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metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='5 % 91.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='7% 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='8% 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='4 % 82.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 % 87.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='3 % 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='5% VGG19 89.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='4 % 94.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='7 % 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2% 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='4 % 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 % 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='4% 89.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='5% 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='4% 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='8 % 75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='7 % 88.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='7 % 88.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='8% Table 2: The trace accuracy of different attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2018), HSJA(Chen, Jordan, and Wainwright 2020) and QEBA(Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2020), the tracing accuracy shows similar results, but for SurFree (Maho, Furon, and Le Merrer 2021), the tracing accuracy will be worse than that of the other attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The reason is that Boundary attack, HSJA(Chen, Jordan, and Wainwright 2020), QEBA(Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2020) are gradient-estimation-based attacks, which tries to use random noise to estimate the gradient of the network and further attack along the gradient.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Since the gradient is highly re- lated to Ti, such attacks are more likely to be trapped by Ti.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' But SurFree(Maho, Furon, and Le Merrer 2021) is at- tacking based on geometric characteristics of the boundary, which may ignore the trap of Ti especially when α is small.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' So compared with Boundary attack(Brendel, Rauber, and Bethge 2018), HSJA(Chen, Jordan, and Wainwright 2020) and QEBA(Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2020), the proposed mechanism may get worse performance when facing SurFree(Maho, Furon, and Le Merrer 2021) attack.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='4 The influence of distributed copy numbers In this section, we will discuss the traceability of the algo- rithm in multiple distributed copies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' When training tracer Ti, the parameter is randomly initialized and each Ti is trained independently.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' So the distribution of DOL corresponding to any two branches should follow independent and identically distribution.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Therefore, the traceability results of multiple copies could be calculated from the results of two copies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In order to verify the correctness, we perform the following experiments.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For experiment verification, we trained 10 different Ti first, then we randomly choose one Ms as the source model to generate the adversarial examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We record the tracing performance on the n, n ∈ [2, 10] models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' To estimate the tracing results for n, n ∈ [2, 10] models, we utilize the Monte-Carlo sampling method in the distri- bution of two models’ DOL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The specific procedure is de- scribed as: 1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We randomly choose one source model Ms and one other victim model Mv as the fundamental models, then we perform the black-box attack on Ms with 1000 different im- ages and record the DOL of Ts and Tv.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We draw the distribution of DOL corresponding to Ts and Tv as the basic distribution, denoted as Ds and Dv, as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 5a- 5c.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 3).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For the tracing results of n, n ∈ [2, 10] models, we conduct the sampling process (take one sample Ss from Ds and n − 1 sample Sn−1 v from Dv) 10000 times.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 4) For each sampling, if Ss > max(Sn−1 v ), we consider it as a correct tracing sample.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We record the total number of correct tracing N n C in 10000 samplings.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The final tracing accuracy of n models can be calculated with N n C/10000.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The results are shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 5d-5f.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The attack we choose is HSJA(Chen, Jordan, and Wainwright 2020), and α is fixed as 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' It can be seen that with the increasing number of distributed copies, the tracing accuracy gradually decreases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' But with 10 branches, it can still maintain more than 90% accuracy for CIFAR10 and GTSRB.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Besides, the estimated tracing performance is almost the same as the actual experi- ment results, which indicates the correctness of our analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 5 Discussion 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 The importance of noise-sensitive loss In the proposed mechanism, making Ti easier to be attacked is the key for tracing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We design the noise-sensitive loss to meet the requirement.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In this section, experiments will be conducted to show the importance of noise-sensitive loss.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We use two randomly initialized tracers as the comparison to conduct the tracing experiment on 1000 adversarial im- ages.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The adversarial attack is set as HSJA(Chen, Jordan, and Wainwright 2020), α is fixed as 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The experimental results are shown in Table 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Attack CIFAR10 GTSRB mini-ImageNet ResNet18 VGG16 ResNet18 VGG16 ResNet50 VGG19 Random 57.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='9% 62.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='4% 53.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='9% 57.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='0% 56.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2% 59.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='8% Proposed 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='3% 97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='9% 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='7% 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='3% 97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='5% 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='4% Table 3: The trace accuracy of HSJA attack with different T .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' It can be seen that without noise-sensitive loss, the trac- ing accuracy of the random initialized tracer only achieves 60%, which is much lower than the proposed noise-sensitive tracer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' This indicates that noise-sensitive loss is very impor- tant in realizing accurate tracing, only setting different pa- rameters of tracer is not enough to trap the attack to result in specific features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='2 Non-transferability and traceability The concept of traceability is related but not equivalent to non-transferability.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' A non-transferable adversarial exam- ple works only on the victim model it is generated from.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Therefore, tracing such non-transferable example may be a straightforward task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' On the other hand, a transferable sample may be generic enough to work on many copies/- models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The task of tracing becomes more meaningful in (a) The distribution of CIFAR10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (b) The distribution of GTSRB.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (c) The distribution of mini-ImageNet.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (d) The tracing results of CIFAR10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (e) The tracing results of GTSRB.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' (f) The tracing results of mini-ImageNet.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Figure 5: The distribution of DOL with HSJA and ResNet backbone and tracing performance of multiple branches.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' this scenario.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Our ability to trace a non-transferable exam- ple demonstrates that the process of adversarial attack intro- duces distinct traceable features which are unique to each victim model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In this sense, traceability can serve as a fail- safe property in defending adversarial attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' There are many defense methods can satisfy non-transferrability, but once the defense fails, the model will not be effectively pro- tected.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' But our experimental results show that for the pro- posed method, even if the defense fails, we still have a cer- tain probability to trace the attacked model, as shown in Table 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We use the data of “ResNet-CIFAR10” task with HSJA (Chen, Jordan, and Wainwright 2020) and QEBA (Li et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 2020) as examples to show the specific tracing results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Attack α NTr NTr(+) Tr Tr(+) Tr Rate Total Rate HSJA 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 672 672 328 313 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='43% 98.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='50% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 973 973 27 19 70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='37% 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='20% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 993 993 7 0 0% 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='30% QEBA 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05 840 840 160 156 97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='50% 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='60% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1 879 879 121 118 97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='52% 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='70% 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15 859 859 141 138 97.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='87% 99.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='7% Table 4: The trace accuracy of different attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In Table 4, NTr and Tr indicate the number of non- transferrable samples and transferrable samples respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' NTr(+) and Tr(+) indicate the number of successful tracing samples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' We can see that for QEBA with α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05, 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='1, and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15, the traceability to transferrable samples is all keep at a high level which is greater than 97%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' As for HSJA, when α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='05, 328 samples can be transferred, and the trace- ability of transferrable examples achieves 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='43%.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' When α = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='15, although the traceability of transferrable exam- ples decreases to 0%, only 7 samples are transferrable.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' So the total tracing rate is still at a high level.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' In general, the pro- posed method either guarantees the high non-transferability or the high tracing accuracy for transferred samples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='3 Limitations and adaptive attacks Although the proposed system maintains certain traceability in the buyers-seller setting, there are still some limitations that need to be addressed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For example, once the attacker finds a way to attack C and bypass Ti, the tracing perfor- mance may degrade.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' But we found that attacking such sys- tem could be a challenging topic itself (in our setting) as the attackers do not have access to all other copies and thus are unable to avoid the differences that our tracer exploits.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Besides, it seems a more adaptive attack also comes with “cost”.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For instance, the approach of attacking C and by- passing Ti would degrade the visual quality of the attack.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' So future work may be paid on how to evade the attack by utilizing such “cost”.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' 6 Conclusion This paper researches a new aspect of defending against ad- versarial attacks that is traceability of adversarial attacks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The techniques derived could aid forensic investigation of known attacks, and provide deterrence to future attacks in the buyers-seller setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' As for the mechanism, we de- sign a framework which contains two related components (model separation and origin tracing) to realize traceabil- ity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' For model separation, we propose a parallel network structure which pairs a unique tracer with the original classi- fier and a noise-sensitive training loss.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Tracer model injects the unique features and ensures the differences between dis- tributed models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' As for origin tracing, we design an output- logits-based tracing mechanism.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' Based on this, the traceabil- ity of the attacked models can be realized when obtaining 400 Source 350 INon-Source 300 250 200 150 100 50 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='5450 400 Source INon-Source 350 300 250 200 150 100 50500 450 Source INon-Source 400 350 300 250 200 150 100 50110 105 Tracing Accuracy ( 100 95 90 ResNet18-R +--ResNet18-S 85 VGG16-R +-- VGG16-S 80 Number of Distributed Models100 Tracing Accuracy (%) 66 98 96 95 94 ResNet18-R --ResNet18-S 93 VGG16-R +--VGG16-S 92 10 Number of Distributed Models110.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='00 100.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='00 Tracing Accuracy 90.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='00 80.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='00 70.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='0 ResNet50-R ResNet50-S 60.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='0 —VGG19-R +-- VGG19-S 50.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content='00 2 3 4 5 D 10 Number of Distributed Modelsthe adversarial examples.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/1tAzT4oBgHgl3EQfRfvj/content/2301.01218v1.pdf'} +page_content=' The experiment of multi-dataset and multi-network model shows that it is possible to achieve traceability through the adversarial examples.' metadata={'source': 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