The transferability of adversarial examples across deep neural networks (DNNs) is the crux of many black-box attacks. Many prior efforts have been devoted to improving the transferability via increasing the diversity in inputs of some substitute models. In this paper, by contrast, we opt for the diversity in substitute models and advocate to attack a Bayesian model for achieving desirable transferability. Deriving from the Bayesian formulation, we develop a principled strategy for possible finetuning, which can be combined with many off-the-shelf Gaussian posterior approximations over DNN parameters. Extensive experiments have been conducted to verify the effectiveness of our method, on common benchmark datasets, and the results demonstrate that our method outperforms recent state-of-the-arts by large margins (roughly 19% absolute increase in average attack success rate on ImageNet), and, by combining with these recent methods, further performance gain can be obtained. Our code: https://github.com/qizhangli/MoreBayesian-attack.

翻译：对抗样本在深度神经网络间的可迁移性是许多黑盒攻击的关键。以往诸多研究致力于通过增加某些替代模型输入的多样性来提升可迁移性。相比之下，本文选择替代模型的多样性，并主张通过攻击贝叶斯模型来实现理想的可迁移性。基于贝叶斯公式，我们开发了一种可微调的原则性策略，该策略可与多种现成的DNN参数高斯后验近似方法相结合。我们在通用基准数据集上进行了大量实验以验证方法的有效性，结果表明我们的方法大幅超越当前最先进水平（在ImageNet上平均攻击成功率绝对提升约19%），并且与这些最新方法结合后可获得进一步性能增益。我们的代码：https://github.com/qizhangli/MoreBayesian-attack。