UniDataPro/anti-spoofing-replay-pc-videos
Anti-spoofing Dataset The dataset consists of 4,714 videos from 4,714 people, each featuring real faces recorded via personal computers. Designed for training data generation and benchmark recognition tasks, it offers diverse spoofing attacks and attack scenarios essential for evaluating facial recognition and liveness detection performance. By including a wide range of replay attacks and video recordings in MP4 and MOV formats, this dataset supports the development and… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/anti-spoofing-replay-pc-videos.
Anti-spoofing Dataset
The dataset consists of 4,714 videos from 4,714 people, each featuring real faces recorded via personal computers. Designed for training data generation and benchmark recognition tasks, it offers diverse spoofing attacks and attack scenarios essential for evaluating facial recognition and liveness detection performance.
By including a wide range of replay attacks and video recordings in MP4 and MOV formats, this dataset supports the development and validation of anti-spoofing technology, recognition algorithms, and biometric systems. - [Get the data](https://unidata.pro/datasets/anti-spoofing-replay-pc-videos/?utm_source=kaggle-org&utm_medium=referral&utm_campaign=anti-spoofing-replay-pc-videos)
Each clip is annotated with age, gender, and ethnicity labels, allowing researchers to build detection algorithms that generalize across different subjects and device types.
Frequently Asked Questions
What types of replay attacks are represented in this dataset?
The dataset contains PC-recorded videos designed for replay attack and liveness detection research. The recordings capture live facial videos that can be used to develop and evaluate models capable of distinguishing genuine users from presentation attacks in desktop biometric authentication systems.
How was the replay attack data collected?
The recordings were collected through crowdsourcing platforms using desktop and laptop computers.
Who can benefit from this biometric anti-spoofing dataset?
The dataset is valuable for biometric security companies, fintech providers, cybersecurity researchers, identity verification platforms, universities, and AI developers working on facial authentication. It supports research into secure login systems and presentation attack detection for desktop environments.
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Researchers can use this dataset to study spoofing attempts, fake faces, and photos attacks, improving the resilience of recognition systems and security technology. The dataset supports a wide range of attack detection tasks, helping innovators design more effective anti-spoofing solutions that enhance identity verification, biometric security, and overall computer vision performance.