Recently, channel-aware steganography has been presented for high security. The corresponding selection-channel-aware (SCA) detecting algorithms have also been proposed for improving the detection performance. In this paper, we propose a novel detecting algorithm of JPEG steganography, where the embedding probability and block evaluation are integrated into the new probability. This probability can embody the change due to data embedding. We choose the same high-pass filters as maximum diversity cascade filter residual (MD-CFR) to obtain different image residuals and a weighted histogram method is used to extract detection features. Experimental results on detecting two typical steganographic methods show that the proposed method can improve the performance compared with the state-of-art methods.

Image deformations caused by different steganographic methods are typically extremely small and highly similar, which makes their detection and identification to be a difficult task. Although recent steganalytic methods using deep learning have achieved high accuracy, they have been made to detect stego images to which specific steganographic methods have been applied. In this letter, a staganalytic method is proposed that uses hierarchical residual neural networks (ResNet), allowing detection (i.e. classification between stego and cover images) and identification of four spatial steganographic methods (i.e. LSB, PVD, WOW and S-UNIWARD). Experimental results show that using hierarchical ResNets achieves a classification rate of 79.71% in quinary classification, which is approximately 23% higher compared to using a plain convolutional neural network (CNN).

In this paper, we propose a novel algorithm called multi-projection ensemble discriminant clustering (MPEDC) for JPEG steganalysis. The scheme makes use of the optimal projection of linear discriminant analysis (LDA) algorithm to get more projection vectors by using the micro-rotation method. These vectors are similar to the optimal vector. MPEDC combines unsupervised K-means algorithm to make a comprehensive decision classification adaptively. The power of the proposed method is demonstrated on three steganographic methods with three feature extraction methods. Experimental results show that the accuracy can be improved using iterative discriminant classification.

Image steganalysis can determine whether the image contains the secret messages. In practice, the number of the cover images is far greater than that of the secret images, so it is very important to solve the detection problem in imbalanced image sets. Currently, SMOTE, Borderline-SMOTE and ADASYN are three importantly synthesized algorithms used to solve the imbalanced problem. In these methods, the new sampling point is synthesized based on the minority class samples. But this research is seldom seen in image steganalysis. In this paper, we find that the features of the majority class sample are similar to those of the minority class sample based on the distribution of the image features in steganalysis. So the majority and minority class samples are both used to integrate the new sample points. In experiments, compared with SMOTE, Borderline-SMOTE and ADASYN, this approach improves detection accuracy using the FLD ensemble classifier.

Image steganography is a technique of embedding secret message into a digital image to securely send the information. In contrast, steganalysis focuses on detecting the presence of secret messages hidden by steganography. The modern approach in steganalysis is based on supervised learning where the training set must include the steganographic and natural image features. But if a new method of steganography is proposed, and the detector still trained on existing methods will generally lead to the serious detection accuracy drop due to the mismatch between training and detecting steganographic method. In this paper, we just attempt to process unsupervised learning problem and propose a detection model called self-learning ensemble discriminant clustering (SEDC), which aims at taking full advantage of the statistical property of the natural and testing images to estimate the optimal projection vector. This method can adaptively select the most discriminative subspace and then use K-means clustering to generate the ultimate class labels. Experimental results on J-UNIWARD and nsF5 steganographic methods with three feature extraction methods such as CC-JRM, DCTR, GFR show that the proposed scheme can effectively classification better than blind speculation.

In order to prevent the synonym substitution breaking the balance among frequencies of synonyms and improve the statistical undetectability, this paper proposed a novel linguistic steganography based on synonym run-length encoding. Firstly, taking the relative word frequency into account, the synonyms appeared in the text are digitized into binary values and expressed in the form of runs. Then, message are embedded into the parities of runs' lengths by self-adaptively making a positive or negative synonym transformation on boundary elements of two adjacent runs, while preserving the number of relative high and low frequency synonyms to reduce the embedding distortion. Experimental results have shown that the proposed synonym run-length encoding based linguistic steganographic algorithm makes fewer changes on the statistical characteristics of cover texts than other algorithms, and enhances the capability of anti-steganalysis.

A secure method for steganography is proposed. Pixel-value differencing (PVD) steganography and bit-plane complexity segmentation (BPCS) steganography have the weakness of generating blocky effects and noise in smooth areas and being detectable with steganalysis. To overcome these weaknesses, a secure bit-plane based steganography method on the spatial domain is presented, which uses a robust measure to select noisy blocks for embedding messages. A matrix embedding technique is also applied to reduce the change of cover images. Given that the statistical property of cover images is well preserved in stego-images, the proposed method is undetectable by steganalysis that uses RS analysis or histogram-based analysis. The proposed method is compared with the PVD and BPCS steganography methods. Experimental results confirm that the proposed method is secure against potential attacks.

Steganography is a technique that conceals the very existence of communication by means of hiding secret messages in innocuous cover objects. We previously developed a steganographic method that uses standard MIDI files (SMFs) as cover objects. Our method could conceal the secret messages in SMFs without changing their sound. We also investigated the effectiveness of our method against steganalysis. This steganalytic research revealed that files embedded using our method are vulnerable to detection, because stego SMFs lose the imprints borne by sequencers. In this study, we describe two improved methods of steganography that enable even stego SMFs to keep the sequencer's imprint. As a result, we improved the resistance of SMFs against steganalysis but there was a slight reduction in the embedding rate.