  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 https://github.com/shuaili1027/MAELS.git.
