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Deep neural networks have achieved remarkable success across various applications; however, their vulnerability to backdoor attacks poses severe security risks -- especially in situations where only a limited set of clean samples is available for defense.
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Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang, “Trojaning attack on neural networks,” in NDSS 2018, San Diego, California, USA, February 18-221, 2018 . The Internet Society, 2018
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B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao, “Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,” in Proceedings of IEEE S&P , 2019
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G. Tao, G. Shen, Y. Liu, S. An, Q. Xu, S. Ma, P. Li, and X. Zhang, “Better trigger inversion optimization in backdoor scanning,” in Conference on Computer Vision and Pattern Recognition (CVPR 2022) , 2022
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