Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration

Juanjuan Weng, Zhiming Luo, Zhun Zhong, Shaozi Li, Nicu Sebe

Abstract: Previous works have extensively studied the transferability of adversarial samples in untargeted black-box scenarios. However, it still remains challenging to craft the targeted adversarial examples with higher transferability than non-targeted ones. Recent studies reveal that the traditional Cross-Entropy (CE) loss function is insufficient to learn transferable targeted perturbations due to the issue of vanishing gradient. In this work, we provide a comprehensive investigation of the CE function and find that the logit margin between the targeted and non-targeted classes will quickly obtain saturated in CE, which largely limits the transferability. Therefore, in this paper, we devote to the goal of enlarging logit margins and propose two simple and effective logit calibration methods, which are achieved by downscaling the logits with a temperature factor and an adaptive margin, respectively. Both of them can effectively encourage the optimization to produce larger logit margins and lead to higher transferability. Besides, we show that minimizing the cosine distance between the adversarial examples and the targeted classifier can further improve the transferability, which is benefited from downscaling logits via L2-normalization. Experiments conducted on the ImageNet dataset validate the effectiveness of the proposed methods, which outperforms the state-of-the-art methods in black-box targeted attacks. The source code for our method is available at https://anonymous.4open.science/r/Target-Attack-72EB/README.md.

Community Implementations: [ 4 code implementations](https://www.catalyzex.com/paper/logit-margin-matters-improving-transferable/code)