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Photo Print Attacks Dataset

Face Anti-Spoofing Liveness dataset of 1K+ people, High-Res Print attacks with Zoom in effect

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Introduction

The Photo Print Attacks Dataset offers a specialized resource for enhancing Presentation Attack Detection (PAD) models, especially suited for assessing liveness detection. With over >5,000 unique individuals, this dataset is an invaluable asset for AI developers aiming to improve anti-spoofing capabilities. Used by both iBeta and NIST FATE, this dataset is structured to support advanced AI model training focused on detecting photo print attacks

Dataset summary

This dataset includes over >5,000 print photo attacks, featuring diverse participants with a balanced representation of gender and ethnicity. Each attack is captured in a 10-20 second video that meets standards for liveness detection, including high-quality imagery and realistic color to simulate authentic conditions

Source and collection methodology

The data collection process involved a large group of participants and carefully staged photo print attacks. Each attack video employs a zoom-in effect, as specified by NIST FATE, enhancing the AI’s ability to recognize print attacks versus live subjects. Flat photos were used to ensure accuracy, with no bending or skewing, providing a consistent, straight-on view of the camera

Use cases and applications

This dataset is ideal for developing and refining liveness detection models that need to reliably differentiate between genuine selfies and photo print attacks. It’s particularly beneficial for organizations working on facial recognition and biometric authentication, aiming to improve the accuracy of spoof detection in PAD systems

Dataset features

  • 1,000+ Participants: Engaged in the project
  • Diverse Representation: Balanced mix of genders and ethnicities
  • 1,000+ Photo Print Attacks: Executed on the participants

Photo print attack description

  • Each attack comprises of 10-20 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)

Download information

A sample version of this dataset is available on Kaggle. Leave a request for additional samples in the form below

Dataset details

This dataset is specifically designed for assessing liveness detection algorithms, as utilized by iBeta and NIST FATE. It is curated to train AI models in recognizing photo print attacks targeting individuals. These attacks encompass Zoom effects, as recommended by NIST FATE to enhance AI training outcomes

  • >1K people
  • 15-20 sec length of Print attacks
  • High-quality photos with realistic colors
  • Various capturing devices were used

Best is used for:

  • Liveness detection
  • Antispoofing attack detection

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  • UAE, Ajman

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