Results indicate that re-morphing-induced similarity change provides a complementary cue for D-MAD, and 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.
Abstract
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.
XSA-MAD, a CLIP-based multimodal framework that explicitly models semantic inconsistencies between bona-fide and morphed faces, is proposed, a CLIP-based multimodal framework that consistently outperforms existing methods under high-fidelity generative attacks.
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MorphUNet is the first diffusion-based morphing framework using trainable parent-separated dual cross-attention inside the denoising U-Net: a Biometric Transport Layer carrying parent-specific identity evidence through denoising, attending to each parent separately before combining residuals via the morphing parameter...
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A multi-dimensional feature fusion-based face anti-spoofing detection method based on the YOLOv8 architecture that integrates dynamic optical flow features with static texture analysis and achieves robust detection through the fusion of spatial and temporal cues.
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Generative diffusion models have revolutionized facial image synthesis, yet robust identity preservation in high resolution outputs remains a critical challenge. This issue is especially vital for security systems, biometric authentication, and privacy sensitive applications, where any drift in identity integrity can u...
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FIDA (Feature Instability-Driven Attack), a novel backdoor attack framework that uses subtle semantic triggers for injection, but its key innovation is a novel objective called Feature Instability Loss, which effectively evades the evaluated perturbation-based defenses.
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Five combined feature-extraction and classification pipelines that address spoof sensitivity and disguise robustness among classical feature representations are proposed, motivating the deep-learning and cross-database extensions discussed in the concluding sections.
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