Summary of Concept Discovery in Deep Neural Networks For Explainable Face Anti-spoofing, by Haoyuan Zhang et al.
Concept Discovery in Deep Neural Networks for Explainable Face Anti-Spoofing
by Haoyuan Zhang, Xiangyu Zhu, Li Gao, Guoying Zhao, Zhen Lei
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new problem in face anti-spoofing, termed X-FAS (eXplainable Face Anti-Spoofing), which enables models to provide explanations for their classifications. The authors incorporate eXplainable Artificial Intelligence (XAI) into face anti-spoofing and propose SPED (SPoofing Evidence Discovery), a method that discovers spoof concepts and provides reliable explanations based on those concepts. To evaluate the quality of X-FAS methods, the authors propose an X-FAS benchmark with annotated spoofing evidence by experts. The paper analyzes SPED’s explanations on face anti-spoofing datasets and compares it to previous XAI methods on the proposed X-FAS benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes face recognition technology more trustworthy by letting machines explain why they think a face is fake or real. Right now, these machines can just say “this face is fake” without telling us why. This isn’t helpful because it doesn’t give us any information about what makes the face fake. The authors of this paper want to fix this problem by making machines that can provide explanations for their answers. They came up with a new way called X-FAS (eXplainable Face Anti-Spoofing) that lets machines explain why they think a face is fake or real. |
Keywords
» Artificial intelligence » Face recognition
