Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks. (arXiv:2305.00399v1 [cs.CR])

Adversarial training (AT) is a robust learning algorithm that can defend
against adversarial attacks in the inference phase and mitigate the side
effects of corrupted data in the training phase. As such, it has become an
indispensable component of many artificial intelligence (AI) systems. However,
in high-stake AI applications, it is crucial to understand AT's vulnerabilities
to ensure reliable deployment. In this paper, we investigate AT's
susceptibility to poisoning attacks, a type of malicious attack that
manipulates training data to …

adversarial adversarial attacks algorithm applications artificial artificial intelligence attacks data high intelligence poisoning side effects systems training vulnerabilities