Steganalysis via a Convolutional Neural Network using Large Convolution Filters for Embedding Process with Same Stego Key

TL;DR

This paper introduces a CNN-based steganalyzer with large convolution filters designed for images with a consistent embedding key, outperforming existing methods and many state-of-the-art steganography schemes.

Contribution

It proposes a novel CNN architecture with larger filters and fewer layers, tailored for images with the same embedding key, enhancing detection performance.

Findings

Outperforms existing CNN-based steganalyzers.

Effective for larger images and lower payloads.

Successfully defeats many state-of-the-art steganography schemes.

Abstract

For the past few years, in the race between image steganography and steganalysis, deep learning has emerged as a very promising alternative to steganalyzer approaches based on rich image models combined with ensemble classifiers. A key knowledge of image steganalyzer, which combines relevant image features and innovative classification procedures, can be deduced by a deep learning approach called Convolutional Neural Networks (CNN). These kind of deep learning networks is so well-suited for classification tasks based on the detection of variations in 2D shapes that it is the state-of-the-art in many image recognition problems. In this article, we design a CNN-based steganalyzer for images obtained by applying steganography with a unique embedding key. This one is quite different from the previous study of {\em Qian et al.} and its successor, namely {\em Pibre et al.} The proposed architecture embeds less convolutions, with much larger filters in the final convolutional layer, and is more general: it is able to deal with larger images and lower payloads. For the "same embedding key" scenario, our proposal outperforms all other steganalyzers, in particular the existing CNN-based ones, and defeats many state-of-the-art image steganography schemes.