Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm. (arXiv:2208.12230v1 [cs.LG])

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 …

adversarial algorithm attack lg