AAAI Conference 2022 Conference Paper
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks
- Francesco Croce
- Maksym Andriushchenko
- Naman D. Singh
- Nicolas Flammarion
- Matthias Hein
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: l0bounded 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. Our code is available at https: //github. com/fra31/sparse-rs.