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For the past few years, in the race between image steganography andsteganalysis, deep learning has emerged as a very promising alternative tosteganalyzer approaches based on rich image models combined with ensembleclassifiers. A key knowledge of image steganalyzer, which combines relevantimage features and innovative classification procedures, can be deduced by adeep learning approach called Convolutional Neural Networks (CNN). These kindof deep learning networks is so well-suited for classification tasks based onthe detection of variations in 2D shapes that it is the state-of-the-art inmany image recognition problems. In this article, we design a CNN-basedsteganalyzer for images obtained by applying steganography with a uniqueembedding key. This one is quite different from the previous study of {\em Qianet al.} and its successor, namely {\em Pibre et al.} The proposed architectureembeds less convolutions, with much larger filters in the final convolutionallayer, and is more general: it is able to deal with larger images and lowerpayloads. For the "same embedding key" scenario, our proposal outperforms allother steganalyzers, in particular the existing CNN-based ones, and defeatsmany state-of-the-art image steganography schemes.

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