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While convolutional neural networks have firmly established themselves as the superior steganography detectors, little human-interpretable feedback to the steganographer as to how the network reaches its decision has so far been obtained from trained models. The folklore has it that, unlike rich models, which rely on global statistics, CNNs can leverage spatially localized signals. In this paper, we adapt existing attribution tools, such as Integrated Gradients and Last Activation Maps, to show that CNNs can indeed find overwhelming evidence for steganography from a few highly localized embedding artifacts. We look at the nature of these artifacts via case studies of both modern content-adaptive and older steganographic algorithms. The main culprit is linked to “content creating changes” when the magnitude of a DCT coefficient is increased (Jsteg, –F5), which can be especially detectable for high frequency DCT modes that were originally zeros (J-MiPOD). In contrast, J- UNIWARD introduces the smallest number of locally detectable embedding artifacts among all tested algorithms. Moreover, we find examples of inhibition that facilitate distinguishing between the selection channels of stego algorithms in a multi-class detector. The authors believe that identifying and characterizing local embedding artifacts provides useful feedback for future design of steganographic schemes. 

In this work, we revisit Perturbed Quantization steganography with modern tools available to the steganographer today, including near-optimal ternary coding and content-adaptive embedding with side-information. In PQ, side-information in the form of rounding errors is manufactured by recompressing a JPEG image with a ju- diciously selected quality factor. This side-information, however, cannotbeusedinthesamefashionasinconventionalside-informed schemes nowadays as this leads to highly detectable embedding. As a remedy, we utilize the steganographic Fisher information to allocate the payload among DCT modes. In particular, we show that the embedding should not be constrained to contributing coef- ficients only as in the original PQ but should be expanded to the so-called “contributing DCT modes.” This approach is extended to color images by slightly modifying the SI-UNIWARD algorithm. Using the best detectors currently available, it is shown that by manufacturing side information with double compression, one can embedthesameamountofinformationintothedoubly-compressed cover image with a significantly better security than applying J- UNIWARD directly in the single-compressed image. At the end of the paper, we show that double compression with the same qual- ity makes side-informed steganography extremely detectable and should be avoided. 

In this paper, we investigate the effect of pretraining CNNs on Ima- geNet on their performance when refined for steganalysis of digital images. In many cases, it seems that just ’seeing’ a large number of images helps with the convergence of the network during the refinement no matter what the pretraining task is. To achieve the best performance, the pretraining task should be related to steganal- ysis, even if it is done on a completely mismatched cover and stego datasets. Furthermore, the pretraining does not need to be carried out for very long and can be done with limited computational re- sources. An additional advantage of the pretraining is that it is done on color images and can later be applied for steganalysis of color and grayscale images while still having on-par or better perfor- mance than detectors trained specifically for a given source. The refining process is also much faster than training the network from scratch. The most surprising part of the paper is that networks pretrained on JPEG images are a good starting point for spatial domain steganalysis as well. 

In this paper, we study the EfficientNet family pre-trained on ImageNet when used for steganalysis using transfer learning. We show that certain “surgical modifications” aimed at maintaining the input resolution in EfficientNet architectures significantly boost their performance in JPEG steganalysis, establishing thus new benchmarks. The modified models are evaluated by their detection accuracy, the number of parameters, the memory consumption, and the total floating point operations (FLOPs) on the ALASKA II dataset. We also show that, surprisingly, EfficientNets in their “vanilla form” do not perform as well as the SRNet in BOSSbase+BOWS2. This is because, unlike ALASKA II images, BOSSbase+BOWS2 contains aggressively subsampled images with more complex content. The surgical modifications in EfficientNet remedy this underperformance as well.