text: 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 and its successor, namely 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.
  @cite_37: The traditional way to represent digital images for feature based steganalysis is to compute a noise residual from the image using a pixel predictor and then form the feature as a sample joint probability distribution of neighboring quantized residual samples-the so - called co-occurrence matrix. In this paper, we propose an alternative statistical representation - instead of forming the co-occurrence matrix, we project neighboring residual samples onto a set of random vectors and take the first-order statistic (histogram) of the projections as the feature. When multiple residuals are used, this representation is called the projection spatial rich model (PSRM). On selected modern steganographic algorithms embedding in the spatial, JPEG, and side-informed JPEG domains, we demonstrate that the PSRM can achieve a more accurate detection as well as a substantially improved performance versus dimensionality trade-off than state-of-the-art feature sets.
  @cite_47: 
  @cite_22: This paper introduces a novel feature set for steganalysis of JPEG images. The features are engineered as first-order statistics of quantized noise residuals obtained from the decompressed JPEG image using 64 kernels of the discrete cosine transform (DCT) (the so-called undecimated DCT). This approach can be interpreted as a projection model in the JPEG domain, forming thus a counterpart to the projection spatial rich model. The most appealing aspect of this proposed steganalysis feature set is its low computational complexity, lower dimensionality in comparison with other rich models, and a competitive performance with respect to previously proposed JPEG domain steganalysis features.
summary: Projection of residuals on random directions @cite_37 in SRM has been applied too in the JPEG domain and is denoted as JPSRM features. With this set of features, the authors obtained detection errors of 0.43 and 0.13 for payloads of 0.1 and 0.4 bpp respectively, with J-UNIWARD and a JPEG Quality Factor set to 0.75. DCTR features @cite_22 have been introduced as an alternative to projection in random directions. In the features calculus, projections are achieved with DCT bases. This change of bases allows to reduce the size of the feature set while preserving its accuracy. When applied on J-UNIWARD with 0.75 JPEG Quality Factor, detection errors are 0.44 and 0.13 for payloads of 0.1 and 0.4 bpp respectively. To the best of our knowledge, the most trustworthy feature set is PHARM @cite_47 . It is a continuation of the DCTR features in the sense that it still takes the position of the residual into the DCT grid. Compared to this previous set, PHARM only considers small prediction kernels to compute residuals. With 0.75 JPEG Quality Factor, detection errors are 0.42 and 0.12 for payloads 0.1 and 0.45 with PHARM features.