A Weakened Joint Reinforcement Method to Improve Robustness of Image Recognition Models

doi:10.3969/j.issn.1006-2475.2023.10.011

Abstract

Abstract: How to enhance the robustness of the model against adversarial examples attacks is an important research direction. In this paper， a method to improve the robustness of image recognition models is proposed. The method consists of a weakening operation and a strengthening operation. The weakening operation weakens the pixel values of the native input and destroys the structure of the adversarial perturbation. This process reduces the adversarial perturbation in the image but also loses some spatial semantic information， and this lost semantic information is supplemented by the reinforcement operation. The reinforcement operation consists of a feature extractor and a feature selector. The feature extractor is used to extract suitable image feature maps， and in order to select robust parts from these feature maps， a feature selector is designed to fuse the content of the feature maps and output feature maps with less perturbation and rich spatial semantic information. In this paper， the effectiveness of the method against adversarial examples is confirmed by extensive comparison experiments and the error accumulation phenomenon of adversarial perturbation is revealed.

Key words: Key words: deep learning, neural networks, adversarial example, image recognition

CLC Number:

TP391

LI Shi-da, XIANG Jian-wen. A Weakened Joint Reinforcement Method to Improve Robustness of Image Recognition Models[J]. Computer and Modernization, 2023, 0(10): 70-76.

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