UniDataPro/silicone-mask-attack

Silicone Mask Attack dataset

The dataset contains 6,500+ videos of attacks from 50 different people, filmed using 5 devices, providing a valuable resource for researching presentation attacks in facial recognition technologies. By focusing on this area, the dataset facilitates experiments designed to improve biometric security and anti-spoofing measures, ultimately aiding in the creation of more robust and reliable authentication systems.

By utilizing this dataset, researchers can develop more accurate liveness detection algorithms, which is crucial for achieving the iBeta Level 2 certification, a benchmark for robust and reliable biometric systems that prevent fraud. - Get the data

Attacks in the dataset

The attacks were recorded in diverse settings, showcasing individuals with various attributes. Each video includes human faces adorned with realistic silicone masks to mimic potential spoofing attempts in facial recognition systems.

Variants of backgrounds and attributes in the dataset:

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Metadata for the dataset

Researchers can learn more about the performance of recognition systems by examining this dataset, which reveals insights into the vulnerabilities of security systems. This data can help improve liveness detection systems, which are independently certified by iBeta, an independent laboratory that assesses the reliability of these systems.

Frequently Asked Questions

What video quality is available in the silicone mask dataset?

The dataset contains high-resolution video recordings ranging from 1920 × 1080 Full HD to 3840 × 2160 4K.

How much environmental variation is included in the dataset?

The dataset contains recordings made against nine different backgrounds. This environmental variation is useful when evaluating whether a model relies on genuine mask-related and facial presentation cues rather than memorizing characteristics of a particular scene.

Who can benefit from the silicone mask attack dataset?

The dataset can benefit biometric security companies, facial recognition engineers, computer vision researchers, liveness detection developers, fintech organizations, identity verification providers, cybersecurity teams, and academic institutions. It is particularly relevant to systems designed to detect physical presentation attacks against face recognition.

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