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Adversarial Sample Generation Method Based on Global Convolution Noise Reduction Model

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Abstract

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.

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Funding

2021 Ministry of Education Industry-University Cooperation Collaborative Education Project, no. 202102348014. 2022 Jiangxi University of Science and Technology Natural Science and Technology Project, no. ZR2101, Project name: Research on blockchain consensus algorithm and its application in IoT data assetization.

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  1. School of Information Engineering, Jiangxi University of Technology, 330098, Nanchang, Jiangxi province, China

    Aiping Cai

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  1. Aiping Cai
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Correspondence to Aiping Cai.

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Aiping Cai Adversarial Sample Generation Method Based on Global Convolution Noise Reduction Model. Aut. Control Comp. Sci. 57, 389–399 (2023). https://doi.org/10.3103/S0146411623040028

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