Photo Print Dataset

Photo Print Attacks Dataset

Face Anti-Spoofing Liveness dataset of 3K+ people and 7K+ High-Res Print attacks with Zoom in effect

Check samples on Kaggle

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 >3,000 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 >7,000 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

How industry leaders achieve superior liveness detection with our dataset

Dataset features

  • 3,000+ Participants: Engaged in the project
  • Diverse Representation: Balanced mix of genders and ethnicities
  • 3,000+ Photo Print Attacks: Executed on the participants
  • This dataset can be integrated into bundles

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)

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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

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We collect data from our internal team. All information is further verified by our specialists

Once your enquiry has been sent, we will contact you to discuss the details and complete the necessary paperwork. The timing of receiving the dataset depends on the specific request and additional requirements

Our unique selling point is to provide legally clean datasets to our customers. We obtain the consent from all the participants to use their data for AI model development. We are able to provide comprensive reporting on the licensing, data collection and privacy compliance of our datasets. Although there seems to be a diverse response to how to control AI development and deployment, we are able to service global customers seeking to launch global AI products.

The dataset follows iBeta testing protocols and includes diverse attack scenarios that mirror real-world spoofing attempts. It covers both passive and active liveness testing requirements with proper demographic representation and standardized capture conditions essential for certification preparation

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