Enhancing image steganalysis with adversarially generated examples

Abstract

The goal of image steganalysis is to counter steganography algorithms which attempt to hide a secret message within an image file. We focus specifically on blind image steganalysis in the spatial domain which involves detecting the presence of secret messages in image files without knowing the exact algorithm used to embed them. In this paper, we demonstrate that we can achieve better performance on the blind steganalysis task by training the YeNet architecture with adversarially generated examples provided by SteganoGAN.