论文标题
稀疏RS：用于查询有效稀疏黑盒对抗攻击的多功能框架
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
论文作者
论文摘要
我们为基于分数的稀疏目标攻击和在黑盒设置中的稀疏目标攻击而提出了一个多功能框架。 Sparse-Rs不依赖替代模型，并为多种稀疏攻击模型实现最先进的成功率和查询效率：$ L_0 $绑定的扰动，对抗性补丁和对抗框架。 $ l_0 $ version的不靶向稀疏-rs的表现优于所有黑色框，甚至所有白色框攻击，用于MNIST，CIFAR-10和IMAGENET上的不同模型。此外，即使对于$ 20 \ times $ 20 $的对抗性补丁和$ 2 $ -pixel宽的对抗框架的挑战性设置，我们的稀疏RS也达到了很高的成功率，价格为$ 224 \ times224 $图像。最后，我们表明可以应用稀疏RS来生成目标的通用对抗贴片，在该贴片中，它大大胜过现有方法。我们的框架代码可从https://github.com/fra31/sparse-rs获得。
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: $l_0$-bounded perturbations, adversarial patches, and adversarial frames. The $l_0$-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 $20\times20$ adversarial patches and $2$-pixel wide adversarial frames for $224\times224$ 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.
