通过重变形检测人脸伪造攻击，提升识别安全性。
Face Re-morphing: Differential Morphing Attack Detection via Feature-Space Similarity Changes
- 利用重变形后特征相似性变化作为检测信号
- 在多个数据集上均优于现有方法，尤其在犯罪场景下表现突出
- 适合用于高安全要求的活体认证系统
人脸融合攻击对人脸识别系统构成严重威胁，因为一张合成图像可能匹配多个身份。差分融合攻击检测（D-MAD）通过对比证件图像与可信活体图像来应对这一威胁，但现有方法多依赖静态特征差异、成员脸重建或多线索融合。本文提出Face Re-morphing，一种基于特征空间对额外融合操作响应的新D-MAD方法。给定证件图像和可信活体图像，该方法生成重融合图像，并以证件-活体与活体-重融合之间的余弦相似度变化作为检测得分。在FRLL-Morphs和FEI Morph数据集上的实验表明，该检测信号在不同融合条件、重融合方法和人脸识别模型下均有效。与现有方法相比，在AMSL上表现良好，且在FEI Morph Version~1的犯罪条件下，使用MorDIFF时性能更优。结果表明，重融合引发的相似性变化为D-MAD提供了互补检测线索。
原文摘要 · Abstract (English)
Face morphing attacks pose a serious threat to face recognition systems because a single morphed document image can be matched to multiple contributors. Differential morphing attack detection (D-MAD) addresses this threat by comparing a document image with a trusted live image, but existing methods often rely on static feature differences, constituent-face reconstruction, or multi-cue fusion. This paper proposes Face Re-morphing, a D-MAD method that uses the feature-space response to an additional morphing operation as a detection cue. Given a document image and a trusted live image, the proposed method generates a re-morphed image and uses the change between the document--live and live--re-morphed cosine similarities as the detection score. Experiments on FRLL-Morphs and FEI Morph show that the proposed cue is effective across different morphing conditions, re-morphing methods, and face recognition models. Comparisons with existing methods show favorable results on AMSL and indicate that the proposed method performs well under the Criminal condition on FEI Morph Version~1, particularly when using MorDIFF. These results indicate that re-morphing-induced similarity change provides a complementary cue for D-MAD.
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