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 [BibTeX] [Marc21]
On the detection of morphing attacks generated by GANs
Type of publication: Conference paper
Citation: Colbois_BIOSIG_2022
Publication status: Accepted
Booktitle: 21st International Conference of the Biometrics Special Interest Group (BIOSIG 2022)
Year: 2022
Month: September
Crossref: Colbois_Idiap-RR-07-2022:
Abstract: 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.
Projects Idiap
Biometrics Center
Authors Colbois, Laurent
Marcel, S├ębastien
Added by: [LC]
Total mark: 0
  • Colbois_BIOSIG_2022.pdf