Coverless Image Steganography Based on Generative Adversarial Network

Jiaohua Qin,Zhibin He,Yun Tan,Huajun Huang,Jing Wang,Xuyu Xiang

doi:10.3390/math8091394

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.

Highlights

Since the invention of the Internet, technology has developed rapidly
We propose a method of using generative adversarial network (GAN) to complete steganography tasks, whose relative payload is
We propose a measurement method to evaluate the image quality of the steganography algorithm based on deep learning, which can be compared with traditional methods

Summary

Introduction

Since the invention of the Internet, technology has developed rapidly. The emergence of multimedia information such as images, audio and video has brought convenience to society [1]but it has resulted in the illegal wiretapping, interception, tampering or destruction of important and sensitive information related to politics, military, finance and business, bringing huge losses to society. The traditional approaches, which adopt artifacts, tend to be detected by automated steganalysis tools and, in extreme cases, by human eyes, which poses the challenge of information hiding. To solve this problem, researchers proposed a new information hiding method—coverless steganography—in 2015. Compared with the traditional approaches, which need to adopt the specified cover image for embedding the secret data, such as Highly Undetectable SteGO (HUGO) and JPEG compression [4,5,6,7], the coverless steganography no longer modifies the cover images, which is why it is called coverless It is achieved by means of mapping with secret information

Methods

Results

Conclusion