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license: cc-by-nc-nd-4.0
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---
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---
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license: cc-by-nc-nd-4.0
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task_categories:
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- video-classification
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- image-to-video
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language:
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- en
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tags:
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- code
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- finance
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- legal
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---
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# Liveness Detection - Video Classification
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The biometric attack dataset with **replay attacks** on the real videos of people. **Replay attack** involves presenting a pre-recorded video or previously captured footage as if it were occurring in real-time.
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The primary objective is to distinguish between genuine, real-time footage and manipulated recordings.
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The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users.
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The dataset contains videos of real humans with various **resolutions, views, and colors**, making it a comprehensive resource for researchers working on anti-spoofing technologies.
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The dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of technologies that can create replays, reflections, and depths, making them susceptible to spoofing and generalization.
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### People in the dataset
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Our dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models.
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# 💴 For Commercial Usage: Full version of the dataset includes 51 000+ videos, leave a request on **[TrainingData](https://trainingdata.pro/data-market/anti-spoofing-replay-attack?utm_source=huggingface&utm_medium=cpc&utm_campaign=display-spoof)** to buy the dataset
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### Metadata for the full dataset:
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- **replay.assignment_id** - unique identifier of the media file
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- **real_assignment_id**- unique identifier of the media file from the [Antispoofing Real Dataset](https://trainingdata.pro/data-market/antispoofing-real?utm_source=kaggle&utm_medium=cpc&utm_campaign=antispoofing-replay-dataset)
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- **worker_id** - unique identifier of the person
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- **age** - age of the person
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- **true_gender** - gender of the person
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- **country** - country of the person
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- **ethnicity** - ethnicity of the person
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- **video_extension** - video extensions in the dataset
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- **video_resolution** - video resolution in the dataset
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- **video_duration** - video duration in the dataset
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- **video_fps** - frames per second for video in the dataset
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# 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/anti-spoofing-replay-attack?utm_source=huggingface&utm_medium=cpc&utm_campaign=display-spoof) to learn about the price and buy the dataset**
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# Content
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The dataset includes **files** folder with videos of people
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### File with the extension .csv
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- **id**: id of the person,
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- **file**: link to access the display spoof attack video
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## **[TrainingData](https://trainingdata.pro/data-market/anti-spoofing-replay-attack?utm_source=huggingface&utm_medium=cpc&utm_campaign=display-spoof)** provides high-quality data annotation tailored to your needs
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More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>**
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TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
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*keywords: liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, ibeta dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset*
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