Datasets:
Languages:
English
Size:
10K<n<100K
Tags:
replay-attack
replay-attack-dataset
face-anti-spoofing
face-liveness-detection
liveness-detection
presentation-attack-detection
License:
| 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](https://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](https://axonlab.ai/dataset/ibeta-level-1-dataset/)** - full Level 1 certification package (paper + replay attacks) | |
| - **[iBeta Level 2 Dataset](https://axonlab.ai/dataset/ibeta-level-2-dataset/)** - 3D mask attack coverage (silicone, latex, wrapped paper) | |
| - **[iBeta Level 3 Dataset](https://axonlab.ai/dataset/ibeta-level-3-dataset/)** - high-fidelity mask attacks | |
| - **[Photo Print Attacks Dataset](https://axonlab.ai/dataset/photo-print-attacks/)** - paper photo spoofing, 3K+ individuals | |
| - **[Silicone Mask Dataset](https://axonlab.ai/dataset/silicone-mask-attack-data/)** - 10K+ videos from 18 silicone masks | |
| Full catalog: [axonlab.ai/datasets](https://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](https://axonlab.ai/) | |
| - Contact: sales@axonlabs.pro | |
| - Full catalog: [axonlab.ai/datasets](https://axonlab.ai/datasets/) | |
| Other Axon Labs datasets on Hugging Face: [huggingface.co/AxonData](https://huggingface.co/AxonData) | |