Cutout print attack dataset
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Introduction
The Cutout 2D Attacks Dataset is a specialized resource aimed at enhancing Presentation Attack Detection (PAD) systems by focusing on cutout photo print attacks. This dataset is designed to support AI developers in training liveness detection models capable of identifying 2D cutout print attacks. This dataset could be utilized by both iBeta and NIST FATE to evaluate and advance anti-spoofing measures
Dataset summary
With contributions from over 2000 individuals, this dataset offers diverse representation across gender and ethnicity. In total 4000+ cutout print attacks, each one is documented in high-quality videos, simulating realistic conditions that provide AI models with the nuanced data required for effective spoof detection
Source and collection methodology
Collected through the participation of more than 2000 individuals, the dataset includes high-quality cutout photos used in attacks. Each attack is captured in a 10-15 second video, with photos presented flat and directly facing the camera to maintain consistency. These controlled conditions support model training by providing a stable view of each attack scenario
Use cases and applications
Ideal for researchers and developers in the field of liveness detection, this dataset enables robust training for models to accurately differentiate between real faces and cutout 2D photo prints. This resource is particularly valuable for those working on facial recognition systems, biometric authentication, and PAD technology
Dataset features
- 2000+ Participants: Engaged in the project
- Diverse Representation: Balanced mix of genders and ethnicities
- 4000+ Cutout Mask Attacks on the participants
Photo print attack description
- Each attack comprises of 10-15 sec. video
- High-quality cutout photos with realistic colors
- Paper attacks conducted on flat photos with a straight view on the camera (not bent or skewed)
Other Datasets for iBeta 1:
Dataset details
This dataset is specifically curated for assessing liveness detection algorithms, such as those used by iBeta and NIST FATE. It is designed to train AI models in identifying a variation of cutout 2D print attacks. These attacks are a crucial aspect of evaluating the effectiveness of liveness detection systems
- >1,5K+ people
- 10-20 sec length of attacks
- High-quality cutout photos with realistic colors
Best is used for:
- Liveness detection
- Antispoofing attack detection
Download information
A sample version of this dataset is available on Kaggle. Leave a request for additional samples in the form below
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