STEGANOGRAPHY USING MACHINE LEARNING

Abstract

Image steganography is a procedure for hiding messages inside pictures. While other techniques such as cryptography aim to prevent adversaries from reading the secret message, steganography aims to hide the presence of the message itself. Steganography must have the ability to resist detection by steganalysis algorithms. Traditional embedding-based steganography embeds the secret information into the content of an image, which unavoidably leaves a trace of the modification that can be detected by increasingly advanced machine-learning-based steganalysis algorithm. In our project, we propose a novel technique for hiding arbitrary binary data in images using generative adversarial networks. We also assess our stego images through image quality assessment and predicted degradation or distortion in our stego image. We also evaluate our approach by measuring its ability to evade deep learning-based steganalysis tools.