Recent Advances of Image Steganography with Generative Adversarial Networks
It addresses the problem of covert communication for researchers and practitioners in steganography, but is incremental as it is a review paper.
This paper reviews image steganography methods using Generative Adversarial Networks (GANs), covering strategies like cover modification, selection, and synthesis, and discusses evaluation metrics and future directions.
In the past few years, the Generative Adversarial Network (GAN) which proposed in 2014 has achieved great success. GAN has achieved many research results in the field of computer vision and natural language processing. Image steganography is dedicated to hiding secret messages in digital images, and has achieved the purpose of covert communication. Recently, research on image steganography has demonstrated great potential for using GAN and neural networks. In this paper we review different strategies for steganography such as cover modification, cover selection and cover synthesis by GANs, and discuss the characteristics of these methods as well as evaluation metrics and provide some possible future research directions in image steganography.