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Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.

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Most current JPEG steganalytic methods can only extract features from a series of linear residuals.Non-linear filters are not considered in these JPEG steganalytic methods, resulting in single types of residuals.Hence, a JPEG steganalytic method using non-linear residuals was proposed.Firstly, non-linear residuals were generated without a high computational cost by using element-wise minimum and maximum operations across a couple of linear residuals which had been obtained in the current JPEG steganalytic method.Secondly, according to the JPEG phase, the non-linear residual was divided into sub-residuals in which the histogram features were extracted.Thirdly, considering the minimum and maximum operators, the symmetrization method was accordingly designed.Finally, all the symmetrized histogram features were concatenated to form the final feature set.Experimental results indicate that the performance for JPEG steganalysis can be improved effectively by using both the linear and the non-linear residuals.

Most current JPEG steganalytic methods can only extract features from a series of linear residuals. Non-linear filters are not considered in these JPEG steganalytic methods, resulting in single types of residuals. Hence, a JPEG steganalytic method using non-linear residuals was proposed. Firstly, non-linear residuals were generated without a high computational cost by using element-wise minimum and maximum operations across a couple of linear residuals which had been obtained in the current JPEG steganalytic method. Secondly, according to the JPEG phase, the non-linear residual was divided into sub-residuals in which the histogram features were extracted. Thirdly, considering the minimum and maximum operators, the symmetrization method was accordingly designed. Finally, all the symmetrized histogram features were concatenated to form the final feature set. Experimental results indicate that the performance for JPEG steganalysis can be improved effectively by using both the linear and the non-linear residuals. [ABSTRACT FROM AUTHOR]

At present, research on steganalysis is mainly focused on the existence detection of hidden messages. However, the extraction of hidden messages, i.e., extraction attacks, also plays a decisive role in tasks such as obtaining evidence of covert communication and fighting against criminal activities. For steganography using a stego key, the purpose of an extraction attack is to recover the stego key. This paper mainly studies methods of recovering the stego key for least significant bit (LSB) steganography in the JPEG domain. Firstly, based on the differences in the statistical properties of the discrete cosine transform (DCT) coefficients between the paths generated by the correct and incorrect keys, a stego key recovery model based on the optimal hypothesis test is proposed. Moreover, formulas are given for calculating the desired sample size and threshold in the proposed stego key recovery model. Secondly, based on the difference in the distributions of odd and even coefficients between the correct and incorrect paths, a method of recovering the stego key for OutGuess steganography using this model is proposed. Finally, based on the difference in the distributions of zero and non-zero coefficients between the correct and incorrect paths, a method of recovering the stego key for F5 steganography (modified version) using this model is proposed. Experimental results for OutGuess 0.13b, OutGuess 0.2 and modified F5 steganography show that the proposed method can successfully recover the stego key and that its performance is superior to that of existing methods. [ABSTRACT FROM AUTHOR]

This paper tackles a recent challenge in identifying culprit actors, who try to hide confidential payload with steganography, among many innocent actors in social media networks. The problem is called steganographer detection problem and is significantly different from the traditional stego detection problem that classifies an individual object as a cover or a stego. To solve the steganographer detection problem over large-scale social media networks, this paper proposes a method that uses high-order joint features and clustering ensembles. It employs 250-D features calculated from the high-order joint matrices of Discrete Cosine Transform (DCT) coefficients of JPEG images, which indicate the dependencies of image content. Furthermore, a number of hierarchical sub-clusterings trained by the features are integrated as a clustering ensemble based on the majority voting strategy, which is used to make optimal decisions on suspicious steganographers. Experimental results show that the proposed scheme is effective and efficient in identifying potential steganographers in large-scale social media networks, and has better performance when tested against the state-of-the-art steganographic methods. [ABSTRACT FROM PUBLISHER]

According to the characteristics of adaptive JPEG steganography which determines the changed DCT coefficients based on embedding distortion, a new steganalysis method by selecting the DCT coefficients with small distortion values is proposed. Firstly, the principle of adaptive JPEG steganography through minimizing distortion is introduced. Secondly, the practicability of selecting the changed DCT coefficients according to distortion values is studied. Thirdly, the proposed steganalysis method is given and the embedding sensitivity of the steganalysis feature extracted from the selected DCT coefficients is analyzed. Lastly, the implement processes of the proposed method are presented and analyzed in details. In the experiments, PQt, PQe and J-UNIWARD steganography are used as examples to verify the effect of the proposed steganalysis method for adaptive JPEG steganography. A serial experimental results show the detection accuracy can be improved obviously, especially when the payload is relatively low. [ABSTRACT FROM AUTHOR]

In real-world steganalysis applications, the traditional steganalysis methods built by a set of training data coming from a source may be applied to detect data from another new different source. In this case, the steganalyzers will face a serious problem that training data and test data are no longer subjected to the same distribution, and thus the detection performance would degrade rapidly. To address this problem, a novel transfer subspace learning method with structure preservation for image steganalysis is proposed in this paper. It aims to alleviate the mismatch between the training and test data so as to improve the detection performance. Specifically, a discriminant projection matrix is learned for the training and test data such that the projected data of both sets lie in a common subspace where each sample can be linearly reconstructed by a combination of the training data. In this way, the difference between the training and test sets is decreased. Further, in order to preserve the structure information of features in the projection subspace, a Frobenius-norm based regularization term is introduced into the objective function. Moreover, to mitigate the negative impacts of noises and outliers, a structurally sparse error matrix is introduced to model the noise and outlier information. The formulation of the proposed method can be efficiently solved by an alternating optimization algorithm. The extensive experiments compared with prior arts show the validity of the proposed method for JPEG image mismatched steganalysis. • A novel transfer learning method is proposed for JPEG image mismatched steganalysis. • A Frobenius-norm regularization is adopted to preserve the structure of features. • A structured sparsity constraint is used to reduce the impact of noises and outliers. • The validity of the proposed method is verified by extensive comparison experiments. [ABSTRACT FROM AUTHOR]

In digital steganography, due to difficulties estimating the JPEG cover image, it is still very hard to accurately locate the hidden message embedded in a JPEG image. Therefore, this study proposes a payload location method for a category of pseudo-random scrambled JPEG image steganography. In order to estimate the quantized discrete cosine transform coefficients in the cover JPEG image, a cover JPEG image estimation method is proposed based on co-frequency sub-image filtering. The proposed payload location method defines a general residual, uses the estimated cover JPEG image to compute the residuals, and then employs the mean residuals of multiple stego images embedded along the same path to distinguish the stego positions. The proposed cover JPEG image estimation method constructs 64 co-frequency sub-images, and then filters the sub-image to estimate the cover JPEG image. Finally, using these methods, payload location algorithms are designed for two common JPEG image steganography algorithms: JSteg and F5. Experimental results show that the proposed location algorithms can effectively locate the stego positions in both JSteg and F5 steganography when the investigator possesses multiple stego images embedded along the same path. In addition, the location results can also be used to recover the steganography key to extract the embedded secret messages. [ABSTRACT FROM AUTHOR]