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@TECHREPORT{Unnervik_Idiap-RR-08-2022,
         author = {Unnervik, Alexander and Marcel, S{\'{e}}bastien},
       keywords = {anomaly detection, Backdoor attack, Biometrics, CNN, Face Recognition, security, trojan attack},
       projects = {Idiap, TRESPASS-ETN},
          month = {8},
          title = {An anomaly detection approach for backdoored neural networks: face recognition as a case study},
           type = {Idiap-RR},
         number = {Idiap-RR-08-2022},
           year = {2022},
    institution = {Idiap},
           note = {Under review [BIOSIG 2022]},
            url = {https://gitlab.idiap.ch/biometric/paper.backdoors_anomaly_detection.biosig2022},
       abstract = {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, conse-
quences 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 detec-
tion 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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2022/Unnervik_Idiap-RR-08-2022.pdf}
}