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arXiv · 2202.02236

Pixle: a fast and effective black-box attack based on rearranging pixels

Jary Pomponi·Simone Scardapane·Aurelio Uncini
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Abstract

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.

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Jary Pomponi, Simone Scardapane, Aurelio Uncini. 2022-02-04. Pixle: a fast and effective black-box attack based on rearranging pixels. https://doi.org/10.1109/ijcnn55064.2022.9892966

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