A Novel Approach Based on Modified Cycle Generative Adversarial Networks for Image Steganography

Abstract

Image steganography aims at hiding information in a cover medium in an imperceptible way. While traditional
steganography methods used invisible inks and microdots, digital world started using images and video files for hiding the secret content in it. Steganalysis is a closely related field for detecting hidden information in these multimedia files. There are many steganography algorithms implemented and tested but most of them fail during Steganalysis. To overcome this issue, in this paper, we are proposing to use generative adversarial networks for image steganography which include discriminative models to identify steganography image during training stage and that helps us to reduce the error rate later during Steganalysis. The proposed modified cycle Generative Adversarial Networks (Mod Cycle GAN) algorithm is tested using the USC-SIPI database and the experimentation results were better when compared with the algorithms in the literature. Because the discriminator block evaluates the image authenticity, we could modify the embedding algorithm until the discriminator could not identify the change made and thereby increasing the robustness.