ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (11): 2260-2270.doi: 10.7544/issn1000-1239.2020.20200437

Special Issue: 2020密码学与数据隐私保护研究专题

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Evolutionary Multi-Objective Optimization Image Steganography Based on Edge Computing

Ding Xuyang1, Xie Ying1,2, Zhang Xiaosong1   

  1. 1(School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731);2(School of Computer Science and Engineering, Southwest Minzu University, Chengdu 610041)
  • Online:2020-11-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61902326) and the Fundamental Research Funds for the Central Universities (Southwest Minzu University) (2020NGD02).

Abstract: Edge computing solves the defect that terminals cannot run complex applications due to limited computing resources. In practical, the edge computing can support terminals with limited computing resources to implement covert communication based on image steganography. This paper proposes an evolutionary multi-objective optimization image steganography based on genetic algorithm, which is suitable for edge computing scenarios. First, a formal definition of image steganography is given by taking steganography imperceptibility optimization and steganography security optimization as objective functions. Secondly, the image is preprocessed through multiple directional and non-directional high-pass filters, and aggregated filter residuals are obtained as candidate locations for embedding of secret information. Then, the genetic manipulations of genetic algorithm are used to iteratively search individuals with high fitness in candidate locations. Through genetic manipulation of the genetic algorithm, the embedded locations with higher fitness is searched iteratively, and the optimal solution of the evolutionary multi-objective optimization problem is obtained. Finally, the secret information is embedded in the pixel locations corresponding to the optimal solution. The simulation experiments are conducted, and results show that the proposed algorithm can maintain image quality and resist steganalysis better than the other existing algorithms.

Key words: edge computing, image steganography, steganalysis, genetic algorithm, multi-objective optimization problem

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