Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm

Xinyi Wang; Simon Yusuf Enoch; Dong Seong Kim

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arXiv · 2208.12230

Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm

Xinyi Wang·Simon Yusuf Enoch·Dong Seong Kim

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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.

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Xinyi Wang, Simon Yusuf Enoch, Dong Seong Kim. 2022-08-25. Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm. https://arxiv.org/abs/2208.12230

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