Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Authors: Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein
Published: 2020-06-23 | Updated: 2022-02-08

Source: https://arxiv.org/abs/2006.12834

PDF: https://arxiv.org/pdf/2006.12834

AIにより推定されたラベル

敵対的攻撃手法 クエリ生成手法 強化学習

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

Abstract

We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on substitute models and achieves state-of-the-art success rate and query efficiency for multiple sparse attack models: l₀-bounded perturbations, adversarial patches, and adversarial frames. The l₀-version of untargeted Sparse-RS outperforms all black-box and even all white-box attacks for different models on MNIST, CIFAR-10, and ImageNet. Moreover, our untargeted Sparse-RS achieves very high success rates even for the challenging settings of 20 × 20 adversarial patches and 2-pixel wide adversarial frames for 224 × 224 images. Finally, we show that Sparse-RS can be applied to generate targeted universal adversarial patches where it significantly outperforms the existing approaches. The code of our framework is available at https://github.com/fra31/sparse-rs.