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1. JPEG Compatibility Attack Revisited

   Dworetzky, Eli; Fridrich, Jessica (January 2021, IEEE transactions on information forensics and security)

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   Abstract--- The JPEG compatibility attack is a steganalysis method for detecting messages embedded in the spatial representation of images under the assumption that the cover is a decompressed JPEG. This paper focuses on improving the detection accuracy for the difficult case of high JPEG qualities and content-adaptive stego algorithms. Close attention is paid to the robustness of the detection with respect to the JPEG compressor and DCT coefficient quantizer. A likelihood ratio detector derived from a model of quantization errors of DCT coefficients in the recompressed image is used to explain the main mechanism responsible for detection and to understand the results of experiments. The most accurate detector is an SRNet trained on a two-channel input consisting of the image and its SQ error. The detection performance is contrasted with state of the art on four content-adaptive stego methods, wide range of payloads and quality factors. 

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2. Revisiting Perturbed Quantization

   https://doi.org/10.1145/3437880.3460396  

   Butora, Jan; Fridrich, Jessica (June 2021, ACM Information Hiding and Multimedia Security Workshop)

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   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. 

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3. Advancing the JPEG Compatibility Attack: Theory, Performance, Robustness, and Practice

   https://doi.org/10.1145/3577163.3595090  

   Dworetzky, E. (June 2023, ACM Information Hiding and Multimedia Security Workshop)

   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. 

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4. Observing Bag Gain in JPEG Batch Steganography

   Kaziakhmedov, E.; Dworetzky, E. (December 2023, IEEE Workshop on Information Security and Forensics)

   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. 

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5. STATISTICAL MODELS FOR IMAGE STEGANOGRAPHY EXPLAINING AND REPLACING HEURISTICS

   Butor, Jan (May 2021, Binghamton University magazine)

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   The steganographic field is nowadays dominated by heuristic approaches for data hiding. While there exist a few model-based steganographic algorithms designed to minimize statistical detectability of the underlying model, many more algorithms based on costs of changing a specific pixel or a DCT coefficient have been over the last decade introduced. These costs are purely heuristic, as they are designed with feedback from detectors implemented as machine learning classifiers. For this reason, there is no apparent relation to statistical detectability, even though in practice they provide comparable security to model-based algorithms. Clearly, the security of such algorithms stands only on the assumption, that the detector used to assess the security, is the best one possible. Such assumption is of course completely unrealistic. Similarly, steganalysis is mainly implemented with empirical machine learning detectors, which use hand-crafted features computed from images or as deep learning detectors - convolutional neural networks. The biggest drawback of this approach is, that the steganalyst, even though having a very good detection power, has very little to no knowledge about what part of the image or the embedding algorithm contributes to the detection, because the detector is used as a black box. In this work, we will try to leave the heuristics behind and go towards statistical models. First, we introduce statistical models for current heuristic algorithms, which helps us understand and predict their security trends. Furthemore this allows us to improve the security of such algorithms. Next, we focus on steganalysis exploiting universal properties of JPEG images. Under certain realistic conditions, this leads to a very powerful attack against any steganography, because embedding even a very small secret message breaks the statistical model. Lastly, we show how we can improve security of JPEG compressed images through additional compression. 

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