Deep neural networks were significantly vulnerable to adversarial examples
manipulated by malicious tiny perturbations. Although most conventional
adversarial attacks ensured the visual imperceptibility between adversarial
examples and corresponding raw images by minimizing their geometric distance,
these constraints on geometric distance led to limited attack transferability,
inferior visual quality, and human-imperceptible interpretability. In this
paper, we proposed a supervised semantic-transformation generative model to
generate adversarial examples with real and legitimate semantics, wherein an
unrestricted adversarial manifold containing continuous semantic variations was
constructed for the first time to realize a legitimate transition from
non-adversarial examples to adversarial ones. Comprehensive experiments on
MNIST and industrial defect datasets showed that our adversarial examples not
only exhibited better visual quality but also achieved superior attack
transferability and more effective explanations for model vulnerabilities,
indicating their great potential as generic adversarial examples. The code and
pre-trained models were available at this https URL

提出了一种有监督的语义转换生成模型，用于生成具有真实和合法语义的对抗性样本，实现了从非对抗性样本到对抗性样本的合法过渡。实验结果表明，生成的对抗性样本不仅具有更好的视觉质量，还实现了更高的攻击可迁移性和更有效的模型漏洞解释。