Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer

Zhijin Ge, Fanhua Shang, Hongying Liu +4

Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs. Although many attack methods can achieve high suc…