  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.
