Adversarial examples (AEs) for DNNs have been shown to be transferable: AEs that successfully fool white-box surrogate models can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable AEs, many of these findings lack explanations and even lead to inconsistent advice. In this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing little robustness phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates, we attribute it to a trade-off between two predominant factors: model smoothness and gradient similarity. Our investigations focus on their joint effects, rather than their separate correlations with transferability. Through a series of theoretical and empirical analyses, we conjecture that the data distribution shift in adversarial training explains the degradation of gradient similarity. Building on these insights, we explore the impacts of data augmentation and gradient regularization on transferability and identify that the trade-off generally exists in the various training mechanisms, thus building a comprehensive blueprint for the regulation mechanism behind transferability. Finally, we provide a general route for constructing better surrogates to boost transferability which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the crucial role of manipulating surrogate models.

翻译：对抗样本（AEs）已被证明具有可迁移性：成功欺骗白盒代理模型的对抗样本也能欺骗具有不同架构的其他黑盒模型。尽管大量实证研究为生成高迁移性对抗样本提供了指导，但许多发现缺乏解释，甚至导致相互矛盾的建议。本文进一步理解对抗迁移性，特别关注代理方面。从有趣的小鲁棒性现象（即使用轻度扰动的对抗样本训练的模型可作为更好的代理）出发，我们将其归因于两个主导因素间的权衡：模型平滑度和梯度相似性。研究聚焦于它们的联合效应，而非它们与迁移性的独立相关性。通过一系列理论与实证分析，我们推测对抗训练中的数据分布偏移解释了梯度相似性的退化。基于这些见解，我们探讨了数据增强和梯度正则化对迁移性的影响，并识别出该权衡普遍存在于各种训练机制中，从而为迁移性背后的调控机制构建了全面的蓝图。最后，我们提供了一条构建更好代理以提升迁移性的通用路径，该路径同时优化模型平滑度和梯度相似性，例如输入梯度正则化与锐度感知最小化（SAM）的组合，并通过大量实验验证。总之，我们呼吁关注这两个因素对发动有效迁移攻击的联合影响，而非优化其中一个而忽略另一个，并强调操控代理模型的关键作用。