http://dl.gi.de/handle/20.500.12116/39684 2026-07-21T13:38:56Z 2026-07-21T13:38:56Z Alexander Unnervik and Sébastien Marcel http://dl.gi.de/handle/20.500.12116/39718 2022-10-27T10:19:34Z 2022-01-01T00:00:00Z

An anomaly detection approach for backdoored neural networks: face recognition as a case study Alexander Unnervik and Sébastien Marcel Brömme, Arslan; Damer, Naser; Gomez-Barrero, Marta; Raja, Kiran; Rathgeb, Christian; Sequeira Ana F.; Todisco, Massimiliano; Uhl, Andreas Backdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not. This hidden functionality can remain inactive for normal use of the algorithm until activated by the attacker. Given how stealthy backdoor attacks are, consequences of these backdoors could be disastrous if such networks were to be deployed for applications as critical as border or access control. In this paper, we propose a novel backdoored network detection method based on the principle of anomaly detection, involving access to the clean part of the training data and the trained network.We highlight its promising potential when considering various triggers, locations and identity pairs, without the need to make any assumptions on the nature of the backdoor and its setup. We test our method on a novel dataset of backdoored networks and report detectability results with perfect scores.

2022-01-01T00:00:00Z Oliver Remy, Jutta Hämmerle-Uhl and Andreas Uhl http://dl.gi.de/handle/20.500.12116/39719 2022-10-27T10:19:34Z 2022-01-01T00:00:00Z

Fingervein Sample Image Quality Assessment using Natural Scene Statistics Oliver Remy, Jutta Hämmerle-Uhl and Andreas Uhl Brömme, Arslan; Damer, Naser; Gomez-Barrero, Marta; Raja, Kiran; Rathgeb, Christian; Sequeira Ana F.; Todisco, Massimiliano; Uhl, Andreas Natural Scene Statistics as used in non-reference image quality measures are proposed to be used as fingervein sample quality indicators. While NIQE and BRISQUE trained on common images with usual distortions do not work well in the fingervein quality context, their variants being trained on high and low quality fingervein sample data behave as expected from a biometric quality estimator. Experiments involve two publicly available fingervein datasets and two distinct template representations. The proposed (trained) quality measures are compared to a set of classical fingervein quality metrics which underlines their highly promising behaviour.

2022-01-01T00:00:00Z Simon Kirchgasser and Andreas Uhl http://dl.gi.de/handle/20.500.12116/39714 2022-10-27T10:19:33Z 2022-01-01T00:00:00Z

Template Protection: On the need to adapt the current Unlinkability Evaluation Protocol Simon Kirchgasser and Andreas Uhl Brömme, Arslan; Damer, Naser; Gomez-Barrero, Marta; Raja, Kiran; Rathgeb, Christian; Sequeira Ana F.; Todisco, Massimiliano; Uhl, Andreas Using ISO/IEC Standards an evaluation protocol exists which properties need to be fulfilled by each template protection scheme. However, in these standards it is not defined how a sensible and sensitive key selection should be done such that the demanded properties are reached. For the analysis regarding the ISO/IEC Standards properties only arbitrary, randomly selected keys are usually used, but it is not considered that in this case the key selection might result in insufficiently protected biometric images (templates). By the performed experiments using not only randomly selected keys, but also considering the best of insufficient (improper) keys, it was revealed that the unlinkability evaluation protocol is influenced by the key selection. Hence, it is recommended to use both mentioned key types to analyse template protection methods. This means that the protocol has to be fine-tuned, in so far that for the best of the worst key choice (which then has to be used in the protocol) unlinkability still needs to be given.

2022-01-01T00:00:00Z Laurent Colbois and Sébastien Marcel http://dl.gi.de/handle/20.500.12116/39715 2022-10-27T10:19:33Z 2022-01-01T00:00:00Z

On the detection of morphing attacks generated by GANs Laurent Colbois and Sébastien Marcel Brömme, Arslan; Damer, Naser; Gomez-Barrero, Marta; Raja, Kiran; Rathgeb, Christian; Sequeira Ana F.; Todisco, Massimiliano; Uhl, Andreas 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.

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