Coverless Image Steganography Based on Generative Adversarial Network

Jiaohua Qin, Jing Wang, Yun Tan, Huajun Huang, Xuyu Xiang and Zhibin He
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Jiaohua Qin: College of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China
Jing Wang: College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410004, China
Yun Tan: College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410004, China
Huajun Huang: College of Information Technology and Management, Hunan University of Finance and Economics, Changsha 410205, China
Xuyu Xiang: College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410004, China
Zhibin He: College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha 410004, China

Mathematics, 2020, vol. 8, issue 9, 1-11

Abstract: Traditional image steganography needs to modify or be embedded into the cover image for transmitting secret messages. However, the distortion of the cover image can be easily detected by steganalysis tools which lead the leakage of the secret message. So coverless steganography has become a topic of research in recent years, which has the advantage of hiding secret messages without modification. But current coverless steganography still has problems such as low capacity and poor quality .To solve these problems, we use a generative adversarial network (GAN), an effective deep learning framework, to encode secret messages into the cover image and optimize the quality of the steganographic image by adversaring. Experiments show that our model not only achieves a payload of 2.36 bits per pixel, but also successfully escapes the detection of steganalysis tools.

Keywords: coverless steganography; deep learning; generative adversarial network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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