让AI识别伪造人脸并说出原因，提升安全性与可信度。
Spoof Trace Discovery for Deep Learning Based Explainable Face Anti-Spoofing
- 通过发现伪造痕迹，让AI解释为何判定为假脸。
- 在专家标注的基准上验证，解释更可靠且可量化。
- 适合需要透明决策的安防、金融等高风险场景。
随着人脸识别在日常生活中的广泛应用，防伪检测变得愈发重要。现有防伪模型虽在多个数据集上达到高分类准确率，但仅能判断“此脸是假的”，缺乏解释能力，难以让用户信服。本文将可解释AI（XAI）引入防伪领域，提出X-FAS（可解释人脸识别防伪）新问题，使模型不仅能识别还能说明原因。我们提出SPTD方法，能自动发现伪造特征并生成可信解释。为此构建了含专家标注伪造痕迹的X-FAS基准，对SPTD进行定量与定性对比分析。实验表明，SPTD能有效生成高质量解释。
原文摘要 · Abstract (English)
With the rapid growth usage of face recognition in people's daily life, face anti-spoofing becomes increasingly important to avoid malicious attacks. Recent face anti-spoofing models can reach a high classification accuracy on multiple datasets but these models can only tell people "this face is fake" while lacking the explanation to answer "why it is fake". Such a system undermines trustworthiness and causes user confusion, as it denies their requests without providing any explanations. In this paper, we incorporate XAI into face anti-spoofing and propose a new problem termed X-FAS (eXplainable Face Anti-Spoofing) empowering face anti-spoofing models to provide an explanation. We propose SPTD (SPoof Trace Discovery), an X-FAS method which can discover spoof concepts and provide reliable explanations on the basis of discovered concepts. To evaluate the quality of X-FAS methods, we propose an X-FAS benchmark with annotated spoof traces by experts. We analyze SPTD explanations on face anti-spoofing dataset and compare SPTD quantitatively and qualitatively with previous XAI methods on proposed X-FAS benchmark. Experimental results demonstrate SPTD's ability to generate reliable explanations.
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