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The bag gain relates to a gain in security due to spreading payload among multiple covers when the steganog- rapher maintains a positive communication rate. This gain is maximal for a certain optimal bag size, which depends on the embedding method, payload spreading strategy, communication rate, and the cover source. Originally discovered and analyzed in the spatial domain, in this paper we study this phenomenon for JPEG images across quality factors. Our experiments and theoretical analysis indicate that the bag gain is more pronounced for higher JPEG qualities, more aggressive batch senders, and for senders maintaining a fixed payload per bag in terms of bits per DCT rather than per non-zero AC DCT. 

The JPEG compatibility attack is a steganalysis method for detect- ing messages embedded in the spatial representation of an image under the assumption that the cover image was a decompressed JPEG. This paper addresses a number of open problems in previous art, namely the lack of theoretical insight into how and why the attack works, low detection accuracy for high JPEG qualities, ro- bustness to the JPEG compressor and DCT coeffjcient quantizer, and real-life performance evaluation. To explain the main mechanism responsible for detection and to understand the trends exhibited by heuristic detectors, we adopt a model of quantization errors of DCT coefficients in the recompressed image, and within a simplified setup, we analyze the behavior of the most powerful detector. Em- powered by our analysis, we resolve the performance defficiencies using an SRNet trained on a two-channel input consisting of the image and its SQ error. This detector is compared with previous state of the art on four content-adaptive stego methods and for a wide range of payloads and quality factors. The last sections of this paper are devoted to studying robustness of this detector with re- spect to JPEG compressors, quantizers, and errors in estimating the JPEG quantization table. Finally, to demonstrate practical usability of this attack, we test our detector on stego images outputted by real steganographic tools available on the Internet. 

This paper addresses how to fairly compare ROCs of ad hoc (or data driven) detectors with tests derived from statistical models of digital media. We argue that the ways ROCs are typically drawn for each detector type correspond to different hypothesis testing problems with different optimality criteria, making the ROCs incomparable. To understand the problem and why it occurs, we model a source of natural images as a mixture of scene oracles and derive optimal detectors for the task of image steganalysis. Our goal is to guarantee that, when the data follows the statistical model adopted for the hypothesis test, the ROC of the optimal detector bounds the ROC of the ad hoc detector. While the results are applicable beyond the field of image steganalysis, we use this setup to point out possi- ble inconsistencies when comparing both types of detectors and explain guidelines for their proper comparison. Experiments on an artificial cover source with a known model with real stegano- graphic algorithms and deep learning detectors are used to confirm our claims. 

This paper addresses how to fairly compare ROCs of ad hoc (or data driven) detectors with tests derived from statistical models of digital media. We argue that the ways ROCs are typically drawn for each detector type correspond to different hypothesis testing problems with different optimality criteria, making the ROCs incomparable. To understand the problem and why it occurs, we model a source of natural images as a mixture of scene oracles and derive optimal detectors for the task of image steganalysis. Our goal is to guarantee that, when the data follows the statistical model adopted for the hypothesis test, the ROC of the optimal detector bounds the ROC of the ad hoc detector. While the results are applicable beyond the field of image steganalysis, we use this setup to point out possi- ble inconsistencies when comparing both types of detectors and explain guidelines for their proper comparison. Experiments on an artificial cover source with a known model with real stegano- graphic algorithms and deep learning detectors are used to confirm our claims. 

While deep learning has revolutionized image steganalysis in terms of performance, little is known about how much modern data-driven detectors can still be improved. In this paper, we approach this difficult and currently wide open question by working with artificial but realistic looking images with a known statistical model that allows us to compute the detectability of modern content-adaptive algorithms with respect to the most powerful detectors. Multiple artificial image datasets are crafted with different levels of content complexity and noise power to assess their influence on the gap between both types of detectors. Experiments with SRNet as the heuristic detector indicate that independent noise contributes less to the performance gap than content of the same MSE. While this loss is rather small for smooth images, it can be quite large for textured images. A network trained on many realizations of a fixed textured scene will, however, recuperate most of the loss, suggesting that networks have the capacity to approximately learn the parameters of a cover source narrowed to a fixed scene. 

While deep learning has revolutionized image steganalysis in terms of performance, little is known about how much modern data-driven detectors can still be improved. In this paper, we approach this difficult and currently wide open question by working with artificial but realistic looking images with a known statistical model that allows us to compute the detectability of modern content-adaptive algorithms with respect to the most powerful detectors. Multiple artificial image datasets are crafted with different levels of content complexity and noise power to assess their influence on the gap between both types of detectors. Experiments with SRNet as the heuristic detector indicate that in dependent noise contributes less to the performance gap than content of the same MSE. While this loss is rather small for smooth images, it can be quite large for textured images. A network trained on many realizations of a fixed textured scene will, however, recuperate most of the loss, suggesting that networks have the capacity to approximately learn the parameters of a cover source narrowed to a fixed scene.