An anomaly detection approach for backdoored neural networks: face recognition as a case study. (arXiv:2208.10231v1 [cs.CV])

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 case detection face recognition networks neural networks recognition study