There are >5K Screen replay attacks using variety of devices
Check samples on Kaggle
The dataset includes selfies from over 1,000 people. Later, a team of over 200 people made 5,000+ replay display attacks based on these selfies. The attacks provide diversity of lighting, devices, and screens.
The Liveness Detection: Display Replay Attacks Dataset is a comprehensive resource designed to improve anti-spoofing technology, specifically in identifying display replay attacks. This dataset features authentic selfies from over 1,000 participants, followed by 5,000+ replay attacks performed by a team of over 200 people. By offering a variety of lighting, devices, and display scenarios, this dataset supports the development of robust liveness detection models
With a balanced representation of gender and ethnicity, the dataset provides over 5,000 replay attack scenarios based on authentic selfies from diverse participants. Each attack involves dynamic camera angles, enabling models to accurately distinguish between live and replayed display images in real-world conditions
The dataset was collected through voluntary contributions of selfies from 1,000+ individuals, each image of high quality (720p or greater) and free of filters. Replay display attacks were then performed on various screens and recorded from multiple angles, with each video lasting at least 12 seconds to simulate realistic attack scenarios
Ideal for developers and researchers focusing on liveness detection, this dataset is particularly useful for training models to identify display replay attacks. Its applications include enhancing facial recognition systems and biometric authentication, as well as improving general anti-spoofing measures within security solutions
A sample version of this dataset is available on Kaggle. Leave a request for additional samples in the form below
Tell us about yourself, and get access to free samples of the dataset
© 2022 – 2024 Copyright protected.
