基于自适应噪声添加的防御对抗样本算法.

Deep neural networks are vulnerable to the attack of adversarial examples.To solve this problem,some works trained networks by adding Gaussian noise to the image,thereby improving the ability of the network to defend adversarial examples.But the method did not consider the sensitivity of the network to different areas in the image when adding noise.To solve this problem,this paper proposed an adversarial training algorithm based on gradient guidance noise addition.When training the network,it added adaptive noise to different areas based on the sensitivity,added large noise to the more sensitive areas,suppressed the sensitivity of the network to image changes,added less noise to the less sensitive areas and improved the network classification accuracy.Compared with the existing algorithms on the Cifar-10 dataset,the experimental results show that the proposed method effectively improves the accuracy of neural networks when classifying adversarial examples. [ABSTRACT FROM AUTHOR]