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@@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+adversary_examples/adversarial_1.png filter=lfs diff=lfs merge=lfs -text
+docs/DeepRobust.png filter=lfs diff=lfs merge=lfs -text
+deeprobust/image/adversary_examples/test_im1.jpg filter=lfs diff=lfs merge=lfs -text
+deeprobust/image/adversary_examples/test_im2.jpg filter=lfs diff=lfs merge=lfs -text
+deeprobust/image/adversary_examples/deepfool_pert.png filter=lfs diff=lfs merge=lfs -text
+deeprobust/image/adversary_examples/deepfool_result.png filter=lfs diff=lfs merge=lfs -text
+deeprobust/image/adversary_examples/deepfool_orig.png filter=lfs diff=lfs merge=lfs -text
+adversary_examples/adversarial.png filter=lfs diff=lfs merge=lfs -text
+adversary_examples/Deeprobust.png filter=lfs diff=lfs merge=lfs -text

deeprobust/graph/black_box.py

@@ -0,0 +1,75 @@
+import torch
+from deeprobust.graph.defense import GCN
+import pickle
+import os.path as osp
+from deeprobust.graph.data import Dataset
+from deeprobust.graph.utils import preprocess
+import os
+
+def load_victim_model(data, model_name='gcn', device='cpu', file_path=None):
+    """load_victim_model.
+
+    Parameters
+    ----------
+    data : deeprobust.graph.Dataset
+        graph data
+    model_name : str
+        victime model name, e.g. ('gcn', 'deepwalk') But currently it only
+        supports gcn as victim model.
+    device : str
+        'cpu' or 'cuda'
+    file_path :
+        if given, the victim model will be loaded from this path.
+    """
+
+    assert model_name == 'gcn', 'Currently only support gcn as victim model...'
+    if file_path is None:
+        # file_path = f'results/saved_models/{data.name}/{model_name}_checkpoint'
+        file_path = 'results/saved_models/{0}/{1}_checkpoint'.format(data.name, model_name)
+    else:
+        file_path = osp.join(file_path, '{}_checkpoint'.format(model_name))
+
+    # Setup victim model
+    if osp.exists(file_path):
+        victim_model = GCN(nfeat=data.features.shape[1], nclass=data.labels.max().item()+1,
+                    nhid=16, dropout=0.5, weight_decay=5e-4, device=device)
+
+        victim_model.load_state_dict(torch.load(file_path, map_location=device))
+        victim_model.to(device)
+        victim_model.eval()
+        return victim_model
+
+    victim_model = train_victim_model(data=data, model_name=model_name,
+                                        device=device, file_path=osp.dirname(file_path))
+    return victim_model
+
+def train_victim_model(data, model_name='gcn', file_path=None, device='cpu'):
+    """Train the victim model (target classifer) and save the model
+    Note that the attacker can only do black query to this model.
+    """
+
+    if file_path is None:
+        file_path = 'results/saved_models/%s/' % data.name
+
+    adj, features, labels = data.adj, data.features, data.labels
+    idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test
+    nfeat = features.shape[1]
+    adj, features, labels = preprocess(adj, features, labels, preprocess_adj=False)
+
+    # Setup victim model
+    victim_model = GCN(nfeat=features.shape[1], nclass=labels.max().item()+1,
+                    nhid=16, dropout=0.5, weight_decay=5e-4, device=device)
+
+    adj = adj.to(device)
+    features = features.to(device)
+    labels = labels.to(device)
+    victim_model = victim_model.to(device)
+    victim_model.fit(features, adj, labels, idx_train, idx_val)
+
+    # save the model
+    if not osp.exists(file_path):
+        os.system('mkdir -p %s' % file_path)
+    torch.save(victim_model.state_dict(), osp.join(file_path, model_name + '_checkpoint'))
+    victim_model.eval()
+    return victim_model
+

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