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Decision-Based Black-Box Attack Specific to Large-Size Images
Decision-based black-box attacks can craft adversarial examples by only querying the target model for hard-label predictions. However, most existing methods are not efficient when attacking large-size images due to optimization difficulty in high-dimensional space, thus consuming lots of queries or obtaining relatively large perturbations. In this paper, we propose a novel decision-based black-box attack to generate adversarial examples, which is Specific to Large-size Image Attack (SLIA). We only perturb on the low-frequency component of discrete wavelet transform (DWT) of an image, reducing the dimension of the gradient to be estimated. Besides, when initializing the adversarial example of the untargeted attack, we remain the high-frequency components of the original image unchanged, and only update the low-frequency component with the randomly sampled uniform noise, thereby reducing the distortion at the beginning of the attack. Extensive experimental results demonstrate that the proposed SLIA outperforms state-of-the-art algorithms when attacking a variety of different threat models. The source code is publicly available at https://github.com/GZHU-DVL/SLIA.