Adversarial Fine-tuning for Backdoor Defense: Connect Adversarial
Examples to Triggered Samples
- URL: http://arxiv.org/abs/2202.06312v1
- Date: Sun, 13 Feb 2022 13:41:15 GMT
- Title: Adversarial Fine-tuning for Backdoor Defense: Connect Adversarial
Examples to Triggered Samples
- Authors: Bingxu Mu and Le Wang and Zhenxing Niu
- Abstract summary: We propose a new Adversarial Fine-Tuning (AFT) approach to erase backdoor triggers.
AFT can effectively erase the backdoor triggers without obvious performance degradation on clean samples.
- Score: 15.57457705138278
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep neural networks (DNNs) are known to be vulnerable to backdoor attacks,
i.e., a backdoor trigger planted at training time, the infected DNN model would
misclassify any testing sample embedded with the trigger as target label. Due
to the stealthiness of backdoor attacks, it is hard either to detect or erase
the backdoor from infected models. In this paper, we propose a new Adversarial
Fine-Tuning (AFT) approach to erase backdoor triggers by leveraging adversarial
examples of the infected model. For an infected model, we observe that its
adversarial examples have similar behaviors as its triggered samples. Based on
such observation, we design the AFT to break the foundation of the backdoor
attack (i.e., the strong correlation between a trigger and a target label). We
empirically show that, against 5 state-of-the-art backdoor attacks, AFT can
effectively erase the backdoor triggers without obvious performance degradation
on clean samples, which significantly outperforms existing defense methods.
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