Backdoor Cleansing with Unlabeled Data. (arXiv:2211.12044v1 [cs.LG])

Due to the increasing computational demand of Deep Neural Networks (DNNs),
companies and organizations have begun to outsource the training process.
However, the externally trained DNNs can potentially be backdoor attacked. It
is crucial to defend against such attacks, i.e., to postprocess a suspicious
model so that its backdoor behavior is mitigated while its normal prediction
power on clean inputs remain uncompromised. To remove the abnormal backdoor
behavior, existing methods mostly rely on additional labeled clean samples.
However, such requirement …

backdoor data