  Adversarial examples are intentionally perturbed images that mislead
classifiers. These images can, however, be easily detected using denoising
algorithms, when high-frequency spatial perturbations are used, or can be
noticed by humans, when perturbations are large. In this paper, we propose
EdgeFool, an adversarial image enhancement filter that learns structure-aware
adversarial perturbations. EdgeFool generates adversarial images with
perturbations that enhance image details via training a fully convolutional
neural network end-to-end with a multi-task loss function. This loss function
accounts for both image detail enhancement and class misleading objectives. We
evaluate EdgeFool on three classifiers (ResNet-50, ResNet-18 and AlexNet) using
two datasets (ImageNet and Private-Places365) and compare it with six
adversarial methods (DeepFool, SparseFool, Carlini-Wagner, SemanticAdv,
Non-targeted and Private Fast Gradient Sign Methods). Code is available at
https://github.com/smartcameras/EdgeFool.git.
