基于边缘计算的进化多目标优化图像隐写算法

丁旭阳; 谢盈; 张小松

doi:10.7544/issn1000-1239.2020.20200437

基于边缘计算的进化多目标优化图像隐写算法

¹(电子科技大学计算机科学与工程学院成都 611731)
²(西南民族大学计算机科学与工程学院成都 610041) (dingxuyang@uestc.edu.cn)

基金项目: 国家自然科学基金项目(61902326)；西南民族大学中央高校基本科研业务费专项资金项目(2020NGD02)

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中图分类号: TP391
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- 发布日期: 2020-10-31

Evolutionary Multi-Objective Optimization Image Steganography Based on Edge Computing

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)

Funds: 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).

摘要

摘要: 边缘计算解决了终端因计算资源有限而不能运行复杂应用的弊端，在实际应用中可以支持计算资源受限的终端实现基于图像隐写的隐蔽通信.提出了一种适用于边缘计算场景的进化多目标优化图像隐写算法，首先将优化隐写不可察觉性和隐写安全性作为目标函数，给出了图像隐写的形式化定义；其次通过多个定向和非定向的高通滤波器对图像进行预处理，得到叠加的滤波残差作为秘密信息嵌入的候选位置；然后利用遗传算法中的基因操作，在候选位置中逐代寻找适应度高的个体以得到进化多目标优化问题的最优解；最后，在最优解对应的像素位置实现秘密信息的嵌入.通过和现有算法的仿真对比分析结果表明：提出的算法能够很好地保持图像质量，具有较好的抵抗隐写分析的能力.
- 边缘计算 /
- 图像隐写 /
- 隐写分析 /
- 遗传算法 /
- 多目标优化问题
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.
- edge computing /
- image steganography /
- steganalysis /
- genetic algorithm /
- multi-objective optimization problem

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