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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.
With contributions from over 1,500 individuals, this dataset offers diverse representation across gender and ethnicity. Each cutout print attack is documented in high-quality videos, simulating realistic conditions that provide AI models with the nuanced data required for effective spoof detection.
Collected through the participation of more than 1,500 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.
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.
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
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