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On the detection of morphing attacks generated by GANs
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2022
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Gesellschaft für Informatik e.V.
Zusammenfassung
Recent works have demonstrated the feasibility of GAN-based morphing attacks that
reach similar success rates as more traditional landmark-based methods. This new type of “deep”
morphs might require the development of new adequate detectors to protect face recognition systems.
We explore simple deep morph detection baselines based on spectral features and LBP histograms
features, as well as on CNN models, both in the intra-dataset and cross-dataset case. We observe
that simple LBP-based systems are already quite accurate in the intra-dataset setting, but struggle
with generalization, a phenomenon that is partially mitigated by fusing together several of those
systems at score-level.We conclude that a pretrained ResNet effective for GAN image detection is the
most effective overall, reaching close to perfect accuracy. We note however that LBP-based systems
maintain a level of interest : additionally to their lower computational requirements and increased
interpretability with respect to CNNs, LBP+ResNet fusions sometimes also showcase increased
performance versus ResNet-only, hinting that LBP-based systems can focus on meaningful signal
that is not necessarily picked up by the CNN detector.