Datasets:
license: cc-by-nc-4.0
pretty_name: Replay Attack Dataset
size_categories:
- 10K<n<100K
task_categories:
- image-classification
- video-classification
language:
- en
tags:
- replay-attack
- replay-attack-dataset
- face-anti-spoofing
- face-liveness-detection
- liveness-detection
- presentation-attack-detection
- pad
- biometrics
- face-recognition
- spoofing
- anti-spoofing
- display-replay
- monitor-replay
- video-replay
- photo-replay
- iso-30107-3
- ibeta-level-1
- idiap-replay-attack-baseline
- ekyc
- identity-verification
- biometric-authentication
Commercial collection of 10,000+ replay attack videos covering both display-based and mobile-based replay spoofing. Captured from 2,500+ unique participants with balanced gender and ethnicity representation. Suitable for training face anti-spoofing models and preparing for iBeta PAD Level 1 certification
Dataset Summary
Replay attacks are one of the most common presentation attack types in face biometric systems. Attackers replay a video or image of a genuine user on a screen (phone, tablet, monitor) to bypass liveness detection. A robust liveness model must see diverse replay scenarios during training
This dataset combines two attack streams into a single training resource:
- Display replay attacks - captured from a variety of screen devices (monitors, laptops, tablets) with diverse lighting conditions
- Mobile replay attacks - captured across 15+ different phone models spanning low-end, mid-range, and high-end segments, including Samsung Galaxy A54, Honor 70, Google Pixel 7
Both attack streams share the same capture protocol: slow camera movement, multi-angle coverage
Dataset Characteristics
| Feature | Value |
|---|---|
| Total attack videos | 10,000+ |
| Unique participants | 2,500+ |
| Attack sub-types | Display replay + Mobile replay |
| Mobile device coverage | 15+ phone models (incl. Galaxy A54, Honor 70, Pixel 7) |
| Camera behavior | Slow movement, multi-angle |
| Gender distribution | Balanced |
| Ethnicity distribution | Balanced |
| iBeta compliance | Level 1 (PAD) |
For commercial use, production deployment, or access to the full 10,000+ video dataset, contact Axon Labs at axonlab.ai
Use Cases
- Face anti-spoofing model training: expand replay attack coverage beyond narrow academic datasets
- iBeta PAD Level 1 preparation: replay attacks are a required category for iBeta Level 1 certification
- Cross-device generalization: evaluate model performance across display types and phone models
- Benchmarking: compare production PAD models on diverse replay scenarios
- Fraud prevention R&D: detect screen replay attempts in KYC and remote onboarding systems
What Makes This Dataset Different
- Both attack streams in one: most public replay datasets cover either display OR mobile, not both
- Realistic mobile coverage: 15+ real phone models
- Commercial license available: suitable for production deployment, not research-only
- Consent-based: all subjects provided explicit consent for AI training use
- Scale: 10,000+ videos is larger than most publicly available replay collections
Related Datasets from Axon Labs
- iBeta Level 1 Dataset - full Level 1 certification package (paper + replay attacks)
- iBeta Level 2 Dataset - 3D mask attack coverage (silicone, latex, wrapped paper)
- iBeta Level 3 Dataset - high-fidelity mask attacks
- Photo Print Attacks Dataset - paper photo spoofing, 3K+ individuals
- Silicone Mask Dataset - 10K+ videos from 18 silicone masks
Full catalog: axonlab.ai/datasets
About Axon Labs
Axon Labs creates high-quality datasets for AI training, with a focus on biometric security, face recognition, and liveness detection
- Website: axonlab.ai
- Contact: sales@axonlabs.pro
- Full catalog: axonlab.ai/datasets
Other Axon Labs datasets on Hugging Face: huggingface.co/AxonData