AxonData/Anti_spoofing_dataset_video_replay_attack

Liveness Detection Dataset: Replay Display Attacks

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Dataset Description

The Liveness Detection Dataset contains selfies of real people along with over 5,000 replay display attacks generated from these selfies. The dataset is designed to aid in the training and evaluation of liveness detection models, which distinguish between real selfies and replay attacks.

Real Life Selfies

• Each person provided one selfie.
• Selfies are at least 720p quality.
• Faces are clear with no filters.

Replay Display Attacks

• The dataset includes 5,000+ replay attacks.
• The attacks vary in lighting, devices, and screens.
• Videos last at least 12 seconds.
• Cameras move slowly, showing attacks from various angles.

Key Features

• Selfies: Over 1,000 individuals shared selfies, balanced in terms of gender and ethnicity.
• Replay Display Attacks: More than 5,000 replay display attacks crafted from these selfies, providing a diverse set of attack types.

Potential Use Cases

This dataset is ideal for training and evaluating models for:

• Liveness Detection: Helping to distinguish between real selfies and spoof attempts (replay attacks).
• Anti-Spoofing: Enhancing security in biometric systems and preventing fake or spoofed face recognition attempts.
• Biometric Authentication: Improving facial recognition security systems.
• Machine Learning and Deep Learning: Assisting researchers in building robust liveness detection models.

Keywords

• Display attacks
• Antispoofing
• Liveness Detection
• Spoof Detection
• Facial Recognition
• Biometric Authentication
• Security Systems
• AI Dataset
• Replay Attack Dataset
• Anti-Spoofing Technology
• Facial Biometrics
• Machine Learning Dataset
• Deep Learning

Contact and Feedback

We welcome your feedback! Feel free to reach out to us and share your experience with this dataset. If you're interested, you can also receive additional samples for free! 😊 Visit us at Axonlabs to request a full version of the dataset for commercial usage.