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International audience; The Reverse JPEG Compatibility Attack can be used for steganalysis of JPEG images compressed with Quality Factor 100 by detecting increased variance of decompression rounding errors. In this work, we point out the dangers associated with this attack by showing that in an uncontrolled environment, the variance can be elevated simply by using a different JPEG compressor. If not careful, the steganalyst can wrongly misclassify cover images. In order to deal with the diversity associated to the devices or softwares generating JPEGs, we propose in this paper to build a deep learning detector trained on a huge dataset of downloaded images. Experimental evaluation shows that such a detector can provide operational false alarms as small as 10^{−4} , while still correctly classifying 90% of stego images. Furthermore, it is shown that this performance is directly applicable to other image datasets. As a side product, we indicate that the attack is not applicable to images developed with a specific JPEG compressor based on the trunc quantization function.

This paper introduces a novel compatibility attack to detect a steganographic message embedded in the DCT domain of a JPEG image at high-quality factors (close to 100). Because the JPEG compression is not a surjective function, i.e. not every DCT blocks can be mapped from a pixel block, embedding a message in the DCT domain can create incompatible blocks. We propose a method to find such a block, which directly proves that a block has been modified during the embedding. This theoretical method provides many advantages such as being completely independent to Cover Source Mismatch, having good detection power, and perfect reliability since false alarms are impossible as soon as incompatible blocks are found. We show that finding an incompatible block is equivalent to proving the infeasibility of an Integer Linear Programming problem. However, solving such a problem requires considerable computational power and has not been reached for 8x8 blocks. Instead, a timing attack approach is presented to perform steganalysis without potentially any false alarms for large computing power., Workshop on Information Hiding and Multimedia Security, ACM, Jun 2023, Chicago, United States

International audience; Contrary to classical steganalysis methods focused on the detection performance, this paper proposes a reliable steganalysis method targeting two goals: prescribing a given, potentially very small, False-Positive (FP) rate while maximizing the detection accuracy. This is the first step toward operational steganalysis where the forensics agent needs an accurate FP rate in order to make a decision. Because JPEG steganalysis at Quality Factor (QF) 100 is associated with an accurate and robust distribution of Cover images by computing the rounding error after JPEG decompression, we use this domain to derive statistical tests associated with theoretical FP rates. A Kolmogorov-Smirnov test applied on different pixel positions of the 8 × 8 block, associated with an aggregation strategy, and a block filtering pre-processing are combined to propose an accurate yet reliable detector for FP going below 10 −4. Experiments are both assessed on synthetic images and the "wild" ALASKA database using J-UNIWARD.; -La stéganalyse consiste à détecter des images contenant un message caché. Nous cherchons ici à développer une méthode de stéganalyse qui permette de certifier la stéganalyse en contrôlant la probabilité de faux positifs associée. Pour cela nous nous focalisons sur des images compressées en JPEG avec un facteur de qualité de 100. Pour ce facteur de qualité la distribution des erreurs d'arrondies après décompression de ces images, lorsqu'elles ne contiennent pas de message caché, est modélisable de manière précise comme un mélange de 60 distributions uniformes indépendantes et de 8 distributions dépendantes. Nous utilisons ensuite cette modélisation pour construire un test de Kolmogorov-Smirnov, qui nous permet de maîtriser les probabilités de faux positifs associées. Nos résultats sur la base d'images ALASKA illustrent l'importance de la sélection des blocs et des pixels associés pour pouvoir contrôler le taux de faux positifs. La comparaison avec d'autres méthodes de stéganalyse met également en évidence le compromis qu'il existe entre la volonté de maîtriser le taux d'erreur et la puissance du détecteur construit.

International audience; In this work we will investigate which JPEG compressor was used to develop JPEG images compressed with quality factor100. We will do this by inspecting the rounding errors of the decompressed image. Under a few simple assumptions, we can derive aprobabilistic distribution of such rounding errors and detect whether this signal from a given JPEG image follow such a distribution. However,JPEG compression can be implemented in many different ways, which can greatly affect the assumptions made. This can lead to severeunderperformance of a forensic detector that is not aware of possible differences caused by different compressors. Our results on Alaska datasetshow that we can create a deep learning detector, which will with accuracy close to 100% correctly classify the JPEG compressor used for theimage compression.; Dans ce travail, nous allons rechercher quel compresseur JPEG a été utilisé pour développer des images JPEG compressées avec un facteur de qualité de 100. Nous le faisons en inspectant les erreurs d'arrondi de l'image décompressée. Sous quelques hypothèses simples, nous pouvons dériver une distribution probabiliste de ces erreurs d'arrondi et détecter si le signal d'une image JPEG donnée suit une telle distribution. Toutefois, la compression JPEG peut être mise en oeuvre de nombreuses fac ¸ons différentes, ce qui peut affecter considérablement les hypothèses formulées. Cela peut conduire à une sous-performance importante d'un détecteur forensique qui peut être sensible au compresseur. Nos résultats sur le jeu de données Alaska montrent que nous pouvons créer un détecteur d'apprentissage profond qui, avec une précision proche de 100%, détermine quel compresseur JPEG a été utilisé pour la compression de l'image.

International audience; In this work we aim to design a steganographic scheme undetectable by the Reverse JPEG Compatibility Attack (RJCA). The RJCA, while only effective for JPEG images compressed with quality factors 99 and 100, was shown to work mainly due to change in variance of the rounding errors after decompression of the DCT coefficients, which is induced by embedding changes incompatible with the JPEG format. One remedy to preserve the aforementioned format is utilizing during the embedding the rounding errors created during the JPEG compression, but no steganographic method is known to be resilient to RJCA without this knowledge. Inspecting the effect of embedding changes on both variance and mean of decompression rounding errors, we propose a steganographic method allowing resistance against RJCA without any side-information. To reach this goal, we propose a distortion metric making all embedding changes within a DCT block dependent, resulting in a lattice-based embedding. Then it turns out it is enough to cleverly pick the side of the (binary) embedding changes through inspection of their effect on the variance of decompression rounding errors and simply use constant costs in order to enforce their sparsity across DCT blocks. To increase security against detectors in the spatial (pixel) domain, we show an easy way of combining the proposed methodology with steganography designed for spatial domain security, further improving the undetectability for quality factor 99. The improvements over existing non-informed steganography are up to 40% in terms of detector's accuracy.

International audience; This paper describes the architecture and training of detectors developed for the ALASKA steganalysis challenge. For each quality factor in the range 60-98, several multi-class tile detectors implemented as SRNets were trained on various combinations of three input channels: luminance and two chrominance channels. To accept images of arbitrary size, the detector for each quality factor was a multi-class multi-layered perceptron trained on features extracted by the tile detectors. For quality 99 and 100, a new "reverse JPEG compatibility attack" was developed and also implemented using the SRNet via the tile detector. Throughout the paper, we explain various improvements we discovered during the course of the competition and discuss the challenges we encountered and trade offs that had to be adopted in order to build a detector capable of detecting steganographic content in a stego source of great diversity.

Title: Solving AX-equations Author: Jan Butora Department: Department of algebra Supervisor: doc. RNDr. Jiří Tůma, DrSc., Department of algebra Abstract: In this work, we present concept of AX-equations and focus on two such equations. Using similiar techniques, we build a theory for both equations, which allows us to express number of their solutions based only on their parameters. Using this theory, we demonstrate on an example that differential steps, used in differential cryptanalysis of modular addition, are not independent. Moreover, based on this theory we introduce and implement fast algorithms for searching solutions. Keywords: differential cryptanalysis, AX-equations, modular addition, carry, sol- vability condition

Title: Discrete Channel Capacity Author: Jan Butora Department: Department of Algebra Supervisor: doc. Mgr. Štěpán Holub, Ph.D. et Ph.D., Department of Algebra Abstract: This Bachelor thesis introduces and examines C.E. Shannon's discrete channel capacity theory, which was first published in 1948 as one of the founding studies in the field of mathematical information theory. In the first place, possible way of information measurement is presented and communication systems are described. Additionally, emphasis is given to discrete noiseless channel and the theorem on calculating the capacity of such channels is examined and proven. Shannon's proof is examined in detail as it contains several non-trivial results in finite differences. Finally, calculation of channel capacity using the theorem is shown in practice. Keywords: difference equations, generating function, channel capacity