Challenge description

In recent years the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized four editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, ICCV 2021, and CVPR 2023, which together have attracted more than 1200 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In addition to physical presentation attacks (PAs), such as printing, replay, and 3D mask attacks, digital face forgery attacks (FAs) are still a threat that seriously endangers the security of face recognition systems. FAs aim to attack faces using digital editing at the pixel level, such as identity transformation, facial expression transformation, attribute editing, and facial synthesis. At present, detection algorithms for these two types of attacks, ``Face Anti-spoofing (FAS)" and ``Deep Fake/Forgery Detection (DeepFake)", are still being studied as independent computer vision tasks, and cannot achieve the functionality of a unified detection model to respond to both types of attacks simultaneously. To give continuity to our efforts in these relevant problems, we are proposing the 5th Face Anti-Spoofing Workshop@CVPR 2024. We analyze different types of attack clues as the main reason for the incompatibility between these two detection. The spoofing clues based on physical presentation attacks are usually caused by color distortion, screen moire patterns, and production traces. In contrast, the forgery clues based on digital editing attacks are usually changes in pixel values. The fifth competition aims to encourage the exploration of common characteristics in these two types of attack clues and promote the research of unified detection algorithms. Fully considering the above difficulties and challenges, we collect a Unified physical-digital Attack dataset, namely UniAttackData, for this fifth edition for algorithm design and competition promotion, including 1,800 participations with 2 and 12 physical and digital attacks, respectively, with a total of 29,706 videos. For more information about the UniAttackData dataset, please refer to [1]; 

[1] Fang, H., Liu, A., Wan, J., Escalera, S., Zhao, C., Zhang, X., ... & Lei, Z. (2023). Unified Physical-Digital Face Attack Detection.  [Link]

Detailed information about the challenge and associated workshop can be found in the challenge page.


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