This repo contains the code for our ICLR 2020 conference paper "Unrestricted Adversarial Perturbations via Semantic Manipulation". A version of this paper was also presented at CVPR 2019 Adversarial Machine Learning workshop in an oral talk "Big but Imperceptible Adversarial Perturbations via Semantic Manipulation".
tldr: We introduce unrestricted perturbations that manipulate semantically meaningful image-based visual descriptors - color and texture - in order to generate effective and photorealistic adversarial examples.
Abstract: Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against adversarial examples which are carefully crafted samples with a small magnitude of the perturbation. Such adversarial perturbations are usually restricted by bounding their