---
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
---

## Liveness Detection Dataset: Printed Photos Attacks

Photo Print attack dataset (3K individuals+) for Presentation Attack Detection level 1 (PAD). Biometric Attack dataset with printed photos attacks which is used by both iBeta and NIST FATE to assess liveness detection algorithms. This dataset is tailored for training AI models to identify photo print attacks on individuals. Print photo attacks include Zoom effects as mandated by NIST FATE for improved AI training

## Full version of dataset is availible for commercial usage - leave a request on our website [Axon Labs](https://axonlabs.pro/) 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)

## 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

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2F99570c6aeb8165c4e6033e8f43d618dd%2FFrame%2072.png?generation=1743572956749101&alt=media)

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