Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$-norms For Better Adversarial Performance. (arXiv:2212.03069v1 [cs.CV])

Adversarial machine learning has been both a major concern and a hot topic
recently, especially with the ubiquitous use of deep neural networks in the
current landscape. Adversarial attacks and defenses are usually likened to a
cat-and-mouse game in which defenders and attackers evolve over the time. On
one hand, the goal is to develop strong and robust deep networks that are
resistant to malicious actors. On the other hand, in order to achieve that, we
need to devise even …

adversarial attack performance under