Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective. (arXiv:2206.12227v1 [cs.CR])

Neural networks have been widely applied in security applications such as
spam and phishing detection, intrusion prevention, and malware detection. This
black-box method, however, often has uncertainty and poor explainability in
applications. Furthermore, neural networks themselves are often vulnerable to
adversarial attacks. For those reasons, there is a high demand for trustworthy
and rigorous methods to verify the robustness of neural network models.
Adversarial robustness, which concerns the reliability of a neural network when
dealing with maliciously manipulated inputs, is …

adversarial networks survey verification