论文标题
损坏数据以消除欺骗性扰动：使用预处理方法来改善系统鲁棒性
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness
论文作者
论文摘要
尽管深层神经网络在分类任务上取得了出色的表现，但最近的研究表明，训练有素的网络可以通过添加微妙的噪音来愚弄。本文通过在自然训练的分类器上应用恢复过程来提出一种新的方法来改善神经网络鲁棒性。在这种方法中，图像将被一些重要的操作员故意破坏，然后在通过分类器之前恢复。 SARGAN-生成对抗网络（GAN）的扩展能够降低雷达信号。本文将表明，Sargan还可以通过消除对抗性效应来恢复损坏的图像。我们的结果表明，这种方法确实提高了自然训练的网络的性能。
Although deep neural networks have achieved great performance on classification tasks, recent studies showed that well trained networks can be fooled by adding subtle noises. This paper introduces a new approach to improve neural network robustness by applying the recovery process on top of the naturally trained classifier. In this approach, images will be intentionally corrupted by some significant operator and then be recovered before passing through the classifiers. SARGAN -- an extension on Generative Adversarial Networks (GAN) is capable of denoising radar signals. This paper will show that SARGAN can also recover corrupted images by removing the adversarial effects. Our results show that this approach does improve the performance of naturally trained networks.
