Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm
Abstract
Widely used deep learning models are found to have poor robustness. Little noises can fool state-of-the-art models into making incorrect predictions. While there is a great deal of high-performance attack generation methods, most of them directly add perturbations to original data and measure them using L_p norms; this can break the major structure of data, thus, creating invalid attacks. In this paper, we propose a black-box attack, which, instead of modifying original data, modifies latent features of data extracted by an autoencoder; then, we measure noises in semantic space to protect the semantics of data. We trained autoencoders on MNIST and CIFAR-10 datasets and found optimal adversarial perturbations using a genetic algorithm. Our approach achieved a 100% attack success rate on the first 100 data of MNIST and CIFAR-10 datasets with less perturbation than FGSM.
Cite
@article{arxiv.2208.12230,
title = {Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm},
author = {Xinyi Wang and Simon Yusuf Enoch and Dong Seong Kim},
journal= {arXiv preprint arXiv:2208.12230},
year = {2022}
}
Comments
8 pages conference paper, accepted for publication in IEEE GLOBECOM 2022