Adversarial Sample Generation Method Based on Global Convolution Noise Reduction Model

Residual network is a deep learning model widely used in various applications of computer vision, such as image processing, semantic classification and video processing. The traditional residual network is susceptible to the interference induced by the adversarial example attack algorithms, which directly affects the security of practical applications. In this work, we propose a new residual neural network based on a global convolutional denoising model. The global convolution is fused with the denoising technique, and the network structure of the global convolution denoising module is redesigned so that the denoising module can be trained end-to-end with the residual neural network. In addition, the gradient information of the network is concealed to enable the neurons to respond to the pixels that are more meaningful to human vision, thus improving the robustness of the network for adversarial example attack algorithms.