Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation

Zeyu Qin, Yanbo Fan, Yi Liu +4

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples, which can produce erroneous predictions by injecting imperceptible perturbations. In this work…