Recent research has found that neural networks are vulnerable to several types of adversarial attacks, where the input samples are modified in such a way that the model produces a wrong prediction that misclassifies the adversarial sample. In this paper we focus on black-box adversarial attacks, that can be performed without knowing the inner structure of the attacked model, nor the training procedure, and we propose a novel attack that is capable of correctly attacking a high percentage of samples by rearranging a small number of pixels within the attacked image. We demonstrate that our attack works on a large number of datasets and models, that it requires a small number of iterations, and that the distance between the original sample and the adversarial one is negligible to the human eye.
@article{arxiv.2202.02236,
title = {Pixle: a fast and effective black-box attack based on rearranging pixels},
author = {Jary Pomponi and Simone Scardapane and Aurelio Uncini},
journal= {arXiv preprint arXiv:2202.02236},
year = {2022}
}