Bin Lin1, Fei Gao2, Wenli Zeng3,*, Jixin Chen4, Cong Zhang5, Qinsheng Zhu6, Yong Zhou4, Desheng Zheng4, Qian Qiu7,5, Shan Yang8
Computer Systems Science and Engineering, Vol.46, No.3, pp. 3075-3085, 2023, DOI:10.32604/csse.2023.034268
Abstract The current adversarial attacks against deep learning models have achieved incredible success in the white-box scenario. However, they often exhibit weak transferability in the black-box scenario, especially when attacking those with defense mechanisms. In this work, we propose a new transfer-based black-box attack called the channel decomposition attack method (CDAM). It can attack multiple black-box models by enhancing the transferability of the adversarial examples. On the one hand, it tunes the gradient and stabilizes the update direction by decomposing the channels of the input example and calculating the aggregate gradient. On the other hand, it helps to escape from local… More >
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