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license: cc-by-4.0
task_categories:
- image-classification
- image-feature-extraction
- video-classification
- image-segmentation
language:
- en
tags:
- liveness detection
- anti-spoofing
- biometrics
- facial recognition
- machine learning
- deep learning
- AI
- paper mask attack
- iBeta certification
- PAD attack
- security
- ibeta
- face recognition
- pad
- authentication
- fraud
size_categories:
- 1K<n<10K
pretty_name: Print Attack Dataset
---
# Liveness Detection Dataset: Photo Print attack dataset (3K individuals+)
## What Is a Print Attack?
A print attack is a 2D presentation attack vector against face recognition and liveness detection systems, where an attacker presents a printed photo of a real person's face to a camera to deceive biometric authentication. Print attacks are the most common and accessible spoofing technique in face anti-spoofing research and represent the entry-level attack class tested in iBeta Level 1 PAD certification under the ISO/IEC 30107-3 standard
Robust face anti-spoofing systems must detect print attacks reliably under varied conditions - different lighting, distances, capture devices, and printing qualities. NIST FATE benchmarks also include print attack scenarios with zoom-in effects to evaluate algorithm performance under camera-distance variation, which is why this dataset includes 15–20 second videos with zoom-in phases
## Full version of dataset is availible for commercial usage - leave a request on our website [Axon Labs](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to purchase the dataset 💰
## Dataset Description:
- 3,000+ Participants: Engaged in the project
- Diverse Representation: Balanced mix of genders and ethnicities
- 7,000+ Photo Print Attacks: Executed on the participants
## Photo Print attack description:
- Each attack comprises of 15-20 sec. video with Zoom in effects
- High-quality photos with realistic colors
- No visible image borders during the Zoom-in phase
- Paper attacks conducted on flat photos with a straight view on the camera (not bent or skewed)
## Academic Baseline Reference
The canonical academic benchmark for print attack anti-spoofing research is the **Idiap Print-Attack Database** ([idiap.ch/en/scientific-research/data/printattack](https://www.idiap.ch/en/scientific-research/data/printattack)), published by the Idiap Research Institute as one of the foundational datasets in face anti-spoofing literature. This commercial dataset extends Idiap's research line with significantly more participants (3,000+ vs Idiap's 50), broader demographic representation, NIST-FATE-compliant zoom-in effects, and modern smartphone capture conditions, designed for production face recognition and liveness detection systems rather than research benchmarks alone
## Potential Use Cases:
Liveness detection: This dataset is ideal for training and evaluating liveness detection models, enabling researchers to distinguish between selfies and photo print attacks with high accuracy
Successfull Spoofing attack on a Liveness test by [Duobango ](https://www.doubango.org/webapps/face-liveness/)
Keywords:
Print photo attack dataset, Antispoofing for AI, Liveness Detection dataset for AI, Spoof Detection dataset, Facial Recognition dataset, Biometric Authentication dataset, AI Dataset, PAD Attack Dataset, Anti-Spoofing Technology, Facial Biometrics, Machine Learning Dataset, Deep Learning |