The secure steganography for hiding images via GAN
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(2020) 2020:46
EURASIP Journal on Image and Video Processing
RESEARCH
Open Access
The secure steganography for hiding images via GAN Zhangjie Fu1,2 , Fan Wang1 and Xu Cheng1* *Correspondence: [email protected] 1 Department of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China Full list of author information is available at the end of the article
Abstract Steganography is one of the important methods in the field of information hiding, which is the technique of hiding secret data within an ordinary file or message in order to avoid the detection of steganalysis models and human eyes. In recent years, many scholars have applied various deep learning networks to the field of steganalysis to improve the accuracy of detection. The rapid improvement of the accuracy of steganalysis models has caused a huge threat to the security of steganography. In addition, another important factor that limits the security of steganography is capacity. The larger the capacity, the worse and more unnatural the visual quality of carrier images after embedded. Therefore, this paper proposes a steganography model—HIGAN, which constructs the encoding network composed of residual blocks to hide the color secret image into another color image of the same size to output a lower distortion and higher visual quality steganographic image. Moreover, it utilizes the adversarial training between the encoder-decoder network and the steganalysis model to improve the ability to resist the detection of steganalysis models based on deep learning. The experimental results show that our proposed model is achievable and effective. Compared with the previous steganography model for hiding color images based on deep learning, the steganography model in this article could achieve steganographic images with higher visual quality and stronger security. Keywords: Steganography, Information hiding, Generative adversarial networks, Deep learning
1 Introduction Information hiding is one of the important ways to ensure the security of information in the network, which can hide secret information into the carrier imperceptibly [1]. It can not only ensure the security of the data itself, but also ensure that the data can be transmitted securely. Steganography, as an important technology of information hiding, has gradually become one of the hot research directions of domestic and foreign scholars. In order to successfully transmit the secret information, the sender hides the secret information into the carrier in an invisible way, and the receiver extracts the secret information from the steganographic images by using the key. Images have become the main carrier of steganography because of its availability and diversity. In recent years, the detection accuracy of steganalysis models based on deep learning (such as GNCNN © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction i
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