SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning
This addresses a security challenge in face verification systems against identity fraud, offering a novel solution with substantial performance gains, though it is incremental in advancing MAD techniques.
The paper tackles the problem of face morphing attack detection (MAD) by proposing SelfMAD, a self-supervised approach that simulates general morphing artifacts to improve generalization and robustness. It significantly outperforms state-of-the-art methods, reducing detection error by over 64% compared to unsupervised competitors and over 66% compared to discriminative models in cross-morph settings.
With the continuous advancement of generative models, face morphing attacks have become a significant challenge for existing face verification systems due to their potential use in identity fraud and other malicious activities. Contemporary Morphing Attack Detection (MAD) approaches frequently rely on supervised, discriminative models trained on examples of bona fide and morphed images. These models typically perform well with morphs generated with techniques seen during training, but often lead to sub-optimal performance when subjected to novel unseen morphing techniques. While unsupervised models have been shown to perform better in terms of generalizability, they typically result in higher error rates, as they struggle to effectively capture features of subtle artifacts. To address these shortcomings, we present SelfMAD, a novel self-supervised approach that simulates general morphing attack artifacts, allowing classifiers to learn generic and robust decision boundaries without overfitting to the specific artifacts induced by particular face morphing methods. Through extensive experiments on widely used datasets, we demonstrate that SelfMAD significantly outperforms current state-of-the-art MADs, reducing the detection error by more than 64% in terms of EER when compared to the strongest unsupervised competitor, and by more than 66%, when compared to the best performing discriminative MAD model, tested in cross-morph settings. The source code for SelfMAD is available at https://github.com/LeonTodorov/SelfMAD.