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Data-free Backdoor Removal based on Channel Lipschitzness. (arXiv:2208.03111v1 [cs.LG])
Aug. 8, 2022, 1:20 a.m. | Runkai Zheng, Rongjun Tang, Jianze Li, Li Liu
cs.CR updates on arXiv.org arxiv.org
Recent studies have shown that Deep Neural Networks (DNNs) are vulnerable to
the backdoor attacks, which leads to malicious behaviors of DNNs when specific
triggers are attached to the input images. It was further demonstrated that the
infected DNNs possess a collection of channels, which are more sensitive to the
backdoor triggers compared with normal channels. Pruning these channels was
then shown to be effective in mitigating the backdoor behaviors. To locate
those channels, it is natural to consider their …
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