Interpretation-Empowered Neural Cleanse for Backdoor Attacks

Liang-Bo Ning, Zeyu Dai, Jingran Su, Chao Pan, Luning Wang, Wenqi Fan, Qing Li

Abstract: Backdoor attacks have posed a significant threat to deep neural networks, highlighting the need for robust defense strategies. Previous research has demonstrated that attribution maps change substantially when exposed to attacks, suggesting the potential of interpreters in detecting adversarial examples. However, most existing defense methods against backdoor attacks overlook the untapped capabilities of interpreters, failing to fully leverage their potential. In this paper, we propose a novel approach called interpretation-empowered neural cleanse (IENC ) for defending backdoor attacks. Specifically, integrated gradient (IG) is adopted to bridge the interpreters and classifiers to reverse and reconstruct the high-quality backdoor trigger. Then, an interpretation-empowered adaptative pruning strategy (IEAPS) is proposed to cleanse the backdoor-related neurons without the pre-defined threshold. Additionally, a hybrid model patching approach is employed to integrate the IEAPS and preprocessing techniques to enhance the defense performance. Comprehensive experiments are constructed on various datasets, demonstrating the potential of interpretations in defending backdoor attacks and the superiority of the proposed method.