Generating Semantic Adversarial Examples via Feature Manipulation. (arXiv:2001.02297v2 [cs.LG] UPDATED)

The vulnerability of deep neural networks to adversarial attacks has been
widely demonstrated (e.g., adversarial example attacks). Traditional attacks
perform unstructured pixel-wise perturbation to fool the classifier. An
alternative approach is to have perturbations in the latent space. However,
such perturbations are hard to control due to the lack of interpretability and
disentanglement. In this paper, we propose a more practical adversarial attack
by designing structured perturbation with semantic meanings. Our proposed
technique manipulates the semantic attributes of images via …

adversarial lg manipulation