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The iBeta Level 1 Paper Attacks Dataset provides a comprehensive collection of paper-based spoofing attacks specifically designed for Presentation Attack Detection (PAD) testing at iBeta Level 1. This dataset is tailored for researchers and developers working on liveness detection, offering a wide range of paper mask attack variations to aid in training AI models for anti-spoofing applications
The dataset includes over 18,000 paper mask attacks performed by 40+ participants, with a balanced representation across gender and ethnicity (Caucasian, Black, and Asian). Each attack sequence is recorded on both iOS and Android devices, offering varied perspectives and multi-frame, 10-second videos to support active liveness detection
The data collection process involved real-life selfies and videos from participants, followed by multiple paper attack types, such as print, cutout, cylinder, and 3D mask attacks. Each video includes zoom-in and zoom-out phases to enhance the dataset’s application in active liveness detection, simulating realistic spoofing attempts.
This dataset is ideal for teams focused on liveness detection and PAD model training. It’s especially valuable for developers preparing their models for iBeta certification, as it includes a comprehensive set of spoofing scenarios required for level 1 testing.
A sample version of this dataset is available on Kaggle. Leave a request for additional samples in the form below
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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