Steganalysis of JPEG images using rich models

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Digital image steganography using universal distortion (2013, methodology): “Whether one embeds Relative payload (bpnzAC) Figure 5: Testing error EOOB for SI-UNIWARD and three other methods with the union of SRMQ1 and JRM and the ensemble classi.er for JPEG quality factors 75, 85, and 95. in the spatial or JPEG domain, the distortion is always computed in the wavelet d”
Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters (2015, methodology): “In literature [17], in order to capture embedding changes more comprehensively, a rich model of DCT coeﬃcients in a JPEG ﬁles is proposed.”
Deep Residual Network for Steganalysis of Digital Images (2019, unknown): “It culminated in what is recognized today as steganalysis with rich models [9], [15], [17], [19], [30], [38], [50], [53] and scalable machine learning [13], [40], [44].”
Universal distortion function for steganography in an arbitrary domain (2014, methodology): “Spatial-domain steganography methods will be analyzed using the Spatial Rich Model (SRM) [10] consisting of 39 symmetrized sub-models quantized with three different quantization factors with a total dimension of 34, 671.11 JPEG-domain methods (including the side-informed algorithms) will be steganal”
Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT (2015, methodology): “The detection accuracy is roughly at the same level as for the 22,510-dimensional JRM.”