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.
@article{arxiv.1910.12227,
title = {EdgeFool: An Adversarial Image Enhancement Filter},
author = {Ali Shahin Shamsabadi and Changjae Oh and Andrea Cavallaro},
journal= {arXiv preprint arXiv:1910.12227},
year = {2020}
}