Face morphing attacks present a significant threat to face recognition systems used in electronic identity enrolment and border control, particularly in single-image morphing attack detection (S-MAD) scenarios where no trusted reference is available. In spite of the vast amount of research on this problem, morph detection systems struggle in cross-dataset scenarios. To address this problem, we introduce a region-aware frequency-based morph detection strategy that drastically improves over strong baseline methods in challenging cross-dataset and cross-morph settings using a lightweight approach. Having observed the separability of bona fide and morph samples in the frequency domain of different facial parts, our approach 1) introduces the concept of residual frequency domain, where the frequency of the signal is decoupled from the natural spectral decay to easily discriminate between morph and bona fide data; 2) additionally, we reason in a global and local manner by combining the evidence from different facial regions in a Markov Random Field, which infers a globally consistent decision. The proposed method, trained exclusively on the synthetic morphing attack detection development dataset (SMDD), is evaluated in challenging cross-dataset and cross-morph settings on FRLL-Morph and MAD22 sets. Our approach achieves an average equal error rate (EER) of 1.85\% on FRLL-Morph and ranks second on MAD22 with an average EER of 6.12\%, while also obtaining a good bona fide presentation classification error rate (BPCER) at a low attack presentation classification error rate (APCER) using only spectral features. These findings indicate that Fourier-domain residual modeling with structured regional fusion offers a competitive alternative to deep S-MAD architectures.

翻译：人脸融合攻击对电子身份注册和边境控制中采用的人脸识别系统构成重大威胁，尤其在缺乏可信参考图像的单图像融合攻击检测（S-MAD）场景中。尽管已有大量相关研究，现有融合检测系统在跨数据集场景中仍面临困难。为解决该问题，我们提出一种区域感知的频域融合检测策略，该方法通过轻量级设计在极具挑战性的跨数据集与跨融合场景中显著超越了强基线方法。通过观察到真实样本与融合样本在不同面部区域的频域中具有可分性，我们的方法：1）提出残差频域概念，将信号频率与自然频谱衰减解耦，从而更易区分融合数据与真实数据；2）进一步通过马尔可夫随机场融合多面部区域的证据进行全局与局部协同推理，从而得出全局一致的决策。所提方法仅在合成融合攻击检测开发数据集（SMDD）上训练，并在FRLL-Morph与MAD22数据集上进行了跨数据集与跨融合场景的严格评估。该方法在FRLL-Morph上取得1.85%的平均等错误率（EER），在MAD22上以6.12%的平均EER位列第二，同时仅使用频谱特征即在较低攻击呈现分类错误率（APCER）下获得了良好的真实呈现分类错误率（BPCER）。这些结果表明，结合结构化区域融合的傅里叶域残差建模为深度S-MAD架构提供了一种具有竞争力的替代方案。