论文标题
像素：基于重排像素的快速有效的黑盒攻击
Pixle: a fast and effective black-box attack based on rearranging pixels
论文作者
论文摘要
最近的研究发现，神经网络容易受到几种类型的对抗性攻击，在这种攻击中，输入样本的修改方式使模型产生错误的预测，从而误解了对抗性样本。在本文中，我们专注于黑框对抗攻击，可以在不知道攻击模型的内部结构或训练程序的情况下执行，我们提出了一种新颖的攻击，能够通过重新安排攻击图像中的少量像素来正确攻击高比例的样本。我们证明了我们的攻击在大量数据集和模型上起作用，这需要少量的迭代，并且原始样本和对抗性的距离之间的距离对人眼可忽略不计。
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.
