Fast Gradient Non-sign Methods
Adversarial attacks make their success in DNNs, and among them, gradient-based algorithms become one of the mainstreams. Based on the linearity hypothesis, under $\ell_\infty$ constraint, $sign$ operation applied to the gradients is a good choice for generating perturbations. However, side-effects from such operation exist since it leads to the bias of direction between real gradients and perturbations. In other words, current methods contain a gap between real gradients and actual noises, which leads to biased and inefficient attacks. Therefore in this paper, based on the Taylor expansion, the bias is analyzed theoretically, and the correction of $sign$, i.e., Fast Gradient Non-sign Method (FGNM), is further proposed. Notably, FGNM is a general routine that seamlessly replaces the conventional $sign$ operation in gradient-based attacks with negligible extra computational cost. Extensive experiments demonstrate the effectiveness of our methods. Specifically, for untargeted black-box attacks, ours outperform them by 27.5% at most and 9.5% on average. For targeted attacks against defense models, it is 15.1% and 12.7%. Our anonymous code is publicly available at https://github.com/yaya-cheng/FGNM
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