论文标题
通用对抗扰动：小图像数据集的效率
Universal Adversarial Perturbations: Efficiency on a small image dataset
论文作者
论文摘要
尽管神经网络在图像分类任务上表现良好，但它们仍然容易受到对抗性扰动的影响，这些扰动可以欺骗神经网络而无需明显地更改输入图像。一篇论文表明了普遍的对抗性扰动的存在，这些扰动将其添加到任何图像中，将以很高的可能性欺骗神经网络。在本文中，我们将尝试重现通用对抗扰动论文的经验，但是在较小的神经网络架构和训练集中，以便能够研究计算出的扰动的效率。
Although neural networks perform very well on the image classification task, they are still vulnerable to adversarial perturbations that can fool a neural network without visibly changing an input image. A paper has shown the existence of Universal Adversarial Perturbations which when added to any image will fool the neural network with a very high probability. In this paper we will try to reproduce the experience of the Universal Adversarial Perturbations paper, but on a smaller neural network architecture and training set, in order to be able to study the efficiency of the computed perturbation.
