Adversarial examples have posed a severe threat to deep neural networks due to their transferable nature. Currently, various works have paid great efforts to enhance the cross-model transferability, which mostly assume the substitute model is trained in the same domain as the target model. However, in reality, the relevant information of the deployed model is unlikely to leak. Hence, it is vital to build a more practical black-box threat model to overcome this limitation and evaluate the vulnerability of deployed models. In this paper, with only the knowledge of the ImageNet domain, we propose a Beyond ImageNet Attack (BIA) to investigate the transferability towards black-box domains (unknown classification tasks). Specifically, we leverage a generative model to learn the adversarial function for disrupting low-level features of input images. Based on this framework, we further propose two variants to narrow the gap between the source and target domains from the data and model perspectives, respectively. Extensive experiments on coarse-grained and fine-grained domains demonstrate the effectiveness of our proposed methods. Notably, our methods outperform state-of-the-art approaches by up to 7.71\% (towards coarse-grained domains) and 25.91\% (towards fine-grained domains) on average. Our code is available at \url{https://github.com/qilong-zhang/Beyond-ImageNet-Attack}.
翻译:Adversarial 实例对深层神经网络构成了严重的威胁,因为它们具有可转移的性质。目前,各种工作都为增强跨模版可转移性做出了巨大努力,其中多数假设替代模型在与目标模型相同的领域受过培训,但实际上,所部署模型的相关信息不太可能泄露。因此,必须建立一个更加实用的黑箱威胁模型,以克服这一限制,并评估所部署模型的脆弱性。在本文中,仅了解图像网域,我们建议采用超越图像网攻击(BIA)来调查向黑箱域(未知的分类任务)的可转移性。具体地说,我们利用基因化模型学习用于破坏低投入图像特征的对抗功能。基于这一框架,我们进一步提出两个变式,分别缩小来源领域和目标领域与数据和模型视角之间的差距。关于粗重和精细调整域的大规模实验显示了我们拟议方法的有效性。特别是,我们的方法在7.71至平均A+++(向平面)的域域中,我们的方法比91国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国-国