ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (3): 548-568.doi: 10.7544/issn1000-1239.2021.20200360

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Recent Advances in Image Steganography Based on Deep Learning

Fu Zhangjie1,2, Li Enlu1, Cheng Xu1, Huang Yongfeng3, Hu Yuting3   

  1. 1(School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044);2(Peng Cheng Laboratory, Shenzhen, Guangdong 518066);3(Department of Electronic Engineering, Tsinghua University, Beijing 100084)
  • Online:2021-03-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (U1836110, 61602253, 61802058) and the Natural Science Foundation of Jiangsu Province (BK20200039).

Abstract: Image steganography is one of the research hotspots of information security. In the early stages of image steganography study, pixel modifications change the statistical properties of the image in a non-negligible way. So it is difficult to resist detection based on high-dimensional statistical characterization. With the development of deep learning, researchers have proposed a number of deep learning-based image steganography methods, which make image modification more imperceptible and the steganography process more intelligent. To further study image steganography technology, this paper provides a thorough review of the image steganography methods based on deep learning. First of all, according to the image steganography process, this paper profoundly analyzes the deep learning-based image steganography methods from three aspects. The first is to introduce the cover image acquisition methods from two perspectives: generative adversarial networks and adversarial samples. The second is to illustrate the steganographic distortion design methods. The third is to expound the methods of generating stego images based on the encoding-decoding network. In addition, the advantages and disadvantages of coverless image steganography are also discussed and summarized in this paper. This kind of method can achieve imperceptible steganography without the cover image, so it has inherent superiority in resisting steganalysis. Finally, we conclude the pros and cons of deep learning-based image steganography methods and coverless image steganography. On the basis of the analysis, we discuss and prospect the future research direction of image steganography.

Key words: image steganography, deep learning, generative adversarial networks, adversarial examples, coverless image steganography

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