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Abstract Recently, a robust steganographic algorithm that achieves errorless robustness against JPEG recompression has been proposed. The method employs a lattice embedding scheme and utilizes the syndrome-trellis code (STC) for practical embedding. However, we have noticed that errorless robust embedding with STC may encounter failures due to modifications on wet coefficients, especially when a high quality factor is used by the compression channel. To solve this problem, we have discovered that using steganographic polar code (SPC) for embedding has better performance in avoiding modifications on wet coefficients. In this paper, we conduct theoretical analysis to prove the better performance of SPC in wet paper embedding. We establish the condition of avoiding modifications on wet coefficients, followed by presenting a recursive calculation method for determining the distribution of columns in the generator matrix of SPC. The findings reveal that SPC can avoid modifications on wet coefficients under a larger number of wet coefficients compared with STC, and therefore we propose a better errorless robust embedding method employing SPC. The experimental results demonstrate that under close security performance, the proposed method achieves a higher success rate compared with embedding with STC. Specifically, when the quality factor of the compressor is 95 and the payload size is 0.4 bpnzac, our method achieves a success rate of 99.85%, surpassing the 91.95% success rate of the embedding with STC.

Recently, a robust steganographic algorithm that achieves errorless robustness against JPEG recompression has been proposed. The method employs a lattice embedding scheme and utilizes the syndrome-trellis code (STC) for practical embedding. However, we have noticed that errorless robust embedding with STC may encounter failures due to modifications on wet coefficients, especially when a high quality factor is used by the compression channel. To solve this problem, we have discovered that using steganographic polar code (SPC) for embedding has better performance in avoiding modifications on wet coefficients. In this paper, we conduct theoretical analysis to prove the better performance of SPC in wet paper embedding. We establish the condition of avoiding modifications on wet coefficients, followed by presenting a recursive calculation method for determining the distribution of columns in the generator matrix of SPC. The findings reveal that SPC can avoid modifications on wet coefficients under a larger number of wet coefficients compared with STC, and therefore we propose a better errorless robust embedding method employing SPC. The experimental results demonstrate that under close security performance, the proposed method achieves a higher success rate compared with embedding with STC. Specifically, when the quality factor of the compressor is 95 and the payload size is 0.4 bpnzac, our method achieves a success rate of 99.85%, surpassing the 91.95% success rate of the embedding with STC. [ABSTRACT FROM AUTHOR]

Previous studies have specifically demonstrated that incorporating high-quality side-information can notably enhance the steganographic security of JPEG images. To obtain more precise side-information estimates, researchers utilize sophisticated deblocking or denoising filters, which enhance security but require a substantial amount of time. The challenge lies in achieving a significant improvement in security and efficiency. This paper introduces a steganographic network named WTSNet, which leverages wavelet transform for estimating side-information through iterative optimization in both the pixel and wavelet domains. The wavelet domain is purposely designed to capture intricate high-frequency texture details and facilitate precise estimation of the side-information. Furthermore, the embedding cost is adjusted based on the polarity of the estimated rounding error, ensuring both secure and efficient performance. Experimental results demonstrate that the proposed scheme substantially enhances the security of conventional JPEG steganography, which relies on additive distortion. Moreover, it outperforms the state-of-the-art JPEG steganography methods based on estimating side-information while consuming less time.

Recent studies have shown that side-information estimation (SIE) via JPEG image deblocking/restoration is effective in enhancing steganographic security when the side-information of JPEG rounding errors is unavailable. The polarity map of deblocking errors can work well in modulating handcrafted embedding costs. However, it may not be easy to design an optimal deblocking method that is universal to enhance security for all images, and it is unclear how to better utilize the polarity map in modulating statistical model-based embedding costs. To circumvent the difficulty of deblocking method design, we propose an optimal polarity map selection (OPMS) method leveraging existing well-performed deblocking methods. OPMS is designed based on polarity-oriented synthetic stego and minimum feature distance, so that the selected optimal polarity map (OPM) ensures a high security performance to each image. Besides, we propose a statistical model-based modulation method to better exploiting OPM in a quaternary quantized Gaussian embedding (QQGE) model. Through shifting the mean of the distribution, QQGE can derive effective modulated embedding probabilities and reduce the number of modified coefficients without increasing coding complexity. Experimental results demonstrate that the proposed overall steganographic method, called OPMS-QQGE, greatly surpasses existing state-of-the-art SIE-based methods in resisting both CNN-based and feature-based steganalyzers. [ABSTRACT FROM AUTHOR]

Background and Objectives: Steganalysis is the study of detecting messages hidden using steganography. Most steganalysis techniques, known as blind steganalysis, focus on extracting and classifying various statistical features from images. Consequently, researchers continually seek to improve the accuracy of blind detection methods. The current study proposes a blind steganalysis technique based on overlapping blocks. Methods: The proposed method began by decomposing the image into identically sized overlapping blocks, then extracted a feature vector from each block. Subsequently, a tree-structured hierarchical clustering technique was used to partition blocks into multiple classes based on extracted features, and a classifier was trained for each class to determine whether a block is from a cover or stego image. The block decomposition process was repeated for each test image, and a classifier was selected based on the block class to make a decision for each block. Furthermore, the majority vote rule was utilized to determine whether the test image is a cover or stego image. Results: The proposed method was evaluated using the INRIA and BOSSbase datasets. Several parameters, including the number of block classes, feature extraction method, block size, and number and block overlapping level, affected the performance of the proposed method. The optimal block size was 64 × 64 by 32 steps, and the number of block classes was set to 16. WOW, S-UNIWARD, PQ, and nsF5 were the steganographic methods employed to evaluate the proposed method. Experimental results indicated that using overlapping instead of nonoverlapping blocks increased the detection of data embedded in both the spatial and Joint Photographic Experts Group (JPEG) domains by an average of over 9%. In addition, the proposed method's accuracy in detecting the S-UNIWARD method was comparable to that of other deep learning-based steganalysis techniques. Conclusion: The concept of using overlapping blocks improves the efficiency of blind steganalysis by providing the benefit of additional and larger blocks. One of the main advantages of the proposed method is comparable detection accuracy and less computational complexity than recent deep learning-based steganalysis techniques. [ABSTRACT FROM AUTHOR]

Currently, algorithms for compressed image steganography mainly embed hidden message by minimizing the resulting distortion or statistical detectability. However, as a result of purely heuristic distortion definitions and numerically solvable equations in statistical models, there are no closed-form solutions for JPEG steganography. The absence of closed-form expression to model JPEG steganography is the main limitation on understanding single image and pool steganalysis behavior. In this study, building upon our previously proposed framework for spatial steganography, we develop a statistical framework for JPEG steganography in which the cover and the hidden message are modeled by multivariate Gaussian distribution. Based on this statistical model, we propose a novel quantized Gaussian JPEG steganography that is able to accomplish embedding using any costs defined in spatial or discrete cosine transform (DCT) domain as well as residual variances. We conduct our experiments using a popular database with different compression qualities to determine the effectiveness of the proposed model. The experimental results show that the proposed model improves the security of previous works and outperforms the state-of-the-art JPEG steganography algorithms. Furthermore, we extend the closed-form expression of single image steganalysis error to pool steganalysis for an omniscience optimal detector. We employ the derived expression to approximate the empirical results of pool steganalysis based on the empirical detection error of single image steganalysis. The practical advantage of the approximation is that even though it is derived based on the adopted statistical model, it is accurate regardless of payload, embedding domain, embedding method, and steganalysis feature as long as the pooling strategy is optimal. In addition to approximation of the error, we employ the proposed model to make predictions about the variance behavior of pool steganalysis error. We mathematically show that the variance increases as the pool size increases in small payloads. The same behavior is observed in experimental results which re-validates our analytical model. We conclude that although pooling improves detector’s performance, it makes the detector less stable in low payloads and high pool sizes.

Social media platform such as WeChat provides rich cover images for covert communication by steganography. However, in order to save band-width, storage space and make images load faster, the images often will be compressed, which makes the image steganography algorithms designed for lossless network channels unusable. Based on DCT and SVD in nonsubsampled shearlet transform domain, a robust JPEG steganography algorithm is proposed, which can resist image compression and correctly extract the embedded secret message from the compressed stego image. First, by combining the advantages of nonsubsampled shearlet transform, DCT and SVD, the construction method for robust embedding domain is proposed. Then, based on minimal distortion principle, the framework of the proposed robust JPEG steganography algorithm is given and the key steps are described in details. The experimental results show that the proposed JPEG steganography algorithm can achieve competitive robustness and anti-detection capability in contrast to the state-of-the-art robust steganography algorithms. Moreover, it can extract the secret message correctly even if the stego image is compressed by WeChat. [ABSTRACT FROM AUTHOR]

Although significant progress has been achieved recently in automatic learning of steganographic cost, the existing methods designed for spatial images cannot be directly applied to JPEG images which are more common media in daily life. The difficulties of migration are mainly caused by the characteristics of the $8\times 8$ DCT mode structure. To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure. It works with the embedding action sampling mechanism under reinforcement learning, where a policy network learns the optimal embedding policies via maximizing the rewards provided by an environment network. Following a domain-transition design paradigm, the policy network is composed of three modules, i.e., pixel-level texture complexity evaluation module, DCT feature extraction module, and mode-wise rearrangement module. These modules operate in serial, gradually extracting useful features from a decompressed JPEG image and converting them into embedding policies for DCT elements, while considering JPEG characteristics including inter-block and intra-block correlations simultaneously. The environment network is designed in a gradient-oriented way to provide stable reward values by using a wide architecture equipped with a fixed preprocessing layer with $8\times 8$ DCT basis filters. Extensive experiments and ablation studies demonstrate that the proposed method can achieve good security performance for JPEG images against both advanced feature-based and modern CNN-based steganalyzers. [ABSTRACT FROM AUTHOR]

Due to a large number of user-uploaded images, social networks have become secure channels for covert communication. However, the JPEG recompression of social networks changes the DCT coefficients of stego images, resulting in the failure of adaptive steganography. To achieve steganography in lossy channels, robust steganography has been proposed. In this letter, the ability against JPEG recompression of robust steganography is further improved by introducing a robustness cost function. For calculating the robustness cost, a robustness model based on the spatial domain calculated from DCT coefficients is firstly proposed. Then the robustness cost is acquired by measuring the distance between the spatial pixels calculated from modified DCT coefficients and the robustness model adjusted spatial pixels. Combining the distortion function and the robustness cost function, the method proposed has considerable robustness performance while maintaining satisfying security performance. Experimental results show that with the maximum reduction of 4.04% on security performance, the algorithm proposed has a significant improvement on robustness performance compared with state-of-the-art robust steganography. [ABSTRACT FROM AUTHOR]

The state of the art steganography approaches strictly assume that the receiver has access to a steganographic key. This limitation was mitigated for images in spacial domain, but the approach does not apply to JPEG images. In this paper, we introduce a keyless steganography scheme for JPEG images. Despite the spatial domain counterpart, our approach for the JPEG domain effectively preserves higher-order statistical models that are used in steganalysis. We show that our approach does not degrade image quality either. The proposed approach is a Side-Information (SI) steganography in the sense that its input is a never-compressed image. Another characteristic of the proposed approach is the separation of the embedding modification and data extraction domains, which can initiate further studies of similar approaches in the future. [ABSTRACT FROM AUTHOR]

Previous studies have specifically demonstrated that incorporating high-quality side-information can notably enhance the steganographic security of JPEG images. To obtain more precise side-information estimates, researchers utilize sophisticated deblocking or denoising filters, which enhance security but require a substantial amount of time. The challenge lies in achieving a significant improvement in security and efficiency. This paper introduces a steganographic network named WTSNet, which leverages wavelet transform for estimating side-information through iterative optimization in both the pixel and wavelet domains. The wavelet domain is purposely designed to capture intricate high-frequency texture details and facilitate precise estimation of the side-information. Furthermore, the embedding cost is adjusted based on the polarity of the estimated rounding error, ensuring both secure and efficient performance. Experimental results demonstrate that the proposed scheme substantially enhances the security of conventional JPEG steganography, which relies on additive distortion. Moreover, it outperforms the state-of-the-art JPEG steganography methods based on estimating side-information while consuming less time. [ABSTRACT FROM AUTHOR]

This paper presents an image Steganography algorithm that can work for cover images of multiple formats. Having a single algorithm for multiple image types provides several advantages. For example, we can apply uniform security policies across all image formats, we can adaptively select the most suitable cover image based on data length, network bandwidth and allowable distortions, etc. We present our algorithm based on the abstract concept of image components that can be adapted for JPEG, Bitmap, TIFF and PNG cover images. To the best of our knowledge, the proposed algorithm is the first Steganography algorithm that can work for multiple cover image formats. In addition, we have utilized concepts like capacity pre-estimation, adaptive partition schemes and data spreading to embed secret data with enhanced security. The proposed method is tested for robustness against Steganalysis with favorable results. Moreover, comparative results for the proposed algorithm are very promising for three different cover image formats.

Social networks are everywhere and currently transmitting very large messages. As a result, transmitting secret messages in such an environment is worth researching. However, the images used in transmitting messages are usually compressed with a JPEG compression channel, which is lossy and damages the transmitted data. Therefore, to prevent secret messages from being damaged, a robust JPEG steganography is urgently needed. In this paper, a secure robust JPEG steganographic scheme based on an autoencoder with an adaptive BCH encoding (Bose-Chaudhuri-Hocquenghem encoding) is proposed. In particular, the autoencoder is first pretrained to fit the transformation relationship between the JPEG image before and after compression by the compression channel. In addition, the BCH encoding is adaptively utilized according to the content of cover image to decrease the error rate of secret message extraction. The DCT (Discrete Cosine Transformation) coefficient adjustment based on practical JPEG channel characteristics further improves the robustness and statistical security. Comparisons with prior state-of-the-art schemes demonstrate that the proposed robust JPEG steganographic algorithm can provide a more robust performance and statistical security. [ABSTRACT FROM AUTHOR]

Batch steganography aims at properly allocating a large payload to multiple covers, so as to keep the whole covert communication at a satisfactory level of security. JPEG is currently one of the most widely used formats for image storage and transmission. This paper presents an efficient JPEG batch steganographic scheme, which allocates the payload in a linear manner w.r.t. a new heuristic measure - the intrinsic energy of JPEG image contents, in which more concerns are with the high frequency components, and the proposed measure could also be easily generalized to cover selection in batch steganographic applications. And a calibration strategy is elaborately designed to balance the security level when JPEG covers of various QFs are involved in JPEG batch steganography. In this way, the proposed scheme can effectively resolve the problem that the statistical undetectability fluctuates dramatically w.r.t. the size and quality factor when the batch set is involved with various image parameters, and consequently maintains the overall security of the practical JPEG batch steganographic system. Experimental results show that the proposed method exhibits security performance superior or comparable to the state-of-the-art batch schemes while maintaining a low computational cost. [ABSTRACT FROM AUTHOR]

JPEG steganography hides secret messages by modifying DCT coefficients in cover images to ensure the undetectability. Currently, most JPEG steganography methods rely on the additive distortion model, assigning adaptive costs to individual coefficients. However, these costs are often designed within a single domain, either DCT or decompressed domain, thereby making the hiding behavior detectable by multi-domain steganalysis features. To improve the steganography security, this paper proposes an universal Dual-Domain JPEG Additive Distortion (DDJAD) framework, which minimize additive distortion in both JPEG and decompressed domain concurrently. A measure to reduce the perceptibility of coefficient modifications within pixel blocks is first formulated and then incorporated into the traditional JPEG steganography model. Finally, the new additive distortion model is solved by fine-tuning existing costs, illustrating the universality of the proposed method. Experimental results showcase the superiority of the proposed method over previous works across various quality factors and payloads, employing typical steganalysis features. • Paradigm Shift : Novel dual-domain model tackles JPEG steganography limits. • Innovative Framework : Enhanced security via new embedding framework. • Advanced Distortion Cost : Innovative cost addresses new paradigm challenges. • Steganalysis Resistance : Scheme superior in resisting steganalysis. [ABSTRACT FROM AUTHOR]

Nowadays, the most prevailing approach to steganography is the minimal embedding distortion framework, which includes an optimizable distortion function for each cover element and an encoding method to minimize the distortion. With the emergence of Syndrome-Trellis Code, the distortion function plays an increasingly important role in modern adaptive image steganography. In this letter, a new distortion function called generalized uniform embedding distortion (GUED) is proposed for JPEG steganography. The proposed GUED consists of the new distortion measures for both Alternating Current (AC) mode and Discrete Cosine Transform (DCT) block, which are represented in a more general exponential model, aiming to flexibly allocate the embedding data so as to minimize the global changes of the statistics of quantized DCT coefficients after embedding. In addition, an empirical rule is developed to determine the parameters of the exponential function according to the payload and quality factor. By exploring the statistics of both DCT and spatial domains, the proposed GUED is shown to be more consistent with the objective of generalized uniform embedding strategy, i.e., maintaining the relative changes of DCT coefficients to be proportional to their coefficients of variations. Extensive experiments demonstrate that the proposed GUED gains significant performance improvements when compared with its original UERD, and outperforms the state-of-the-art J-UNIWARD with markedly reduced computation time. [ABSTRACT FROM AUTHOR]

Steganography aims to embed additional data into digital media secretly and minimize the distortion caused by data embedding. Up to now, the key point of minimal distortion embedding framework is to design proper distortion function. In this paper, a distortion function for JPEG steganography based on residual blocks is proposed. To obtain less statistical detectability, Residual Block Values (termed as RBVs for short) and quantization steps are both involved in the proposed distortion function. RBVs are exploited to determine the embedding risk caused by modifications in corresponding DCT block while quantization steps are exploited to determine the embedding risk of the selection channel in a block. By implementing the syndrome trellis coding (STC) to embed secret data, the modifications are constrained in hard-to-detect religions. Experiments show that the proposed method performs better than current state-of-the-art methods of JPEG steganography. [ABSTRACT FROM AUTHOR]

Nowadays, JPEG steganographic schemes, e.g., J-UNIWARD, which take into account the effects of embedding in the spatial domain tend to exhibit higher security and introduce less artifacts that can be captured by the prevalent steganalyzers. Following the paradigm, this letter proposes a new design of the distortion measure for JPEG steganography by incorporating the statistics of both the spatial and discrete cosine transform (DCT) domains. The spatial statistics of the decompressed JPEG images are first well characterized with distortion measures of some efficient steganographic schemes in the spatial domain, e.g., HILL, and the resulting embedding entropies of spatial blocks in alignment with DCT blocks are then transformed into the DCT domain to obtain the distortion measures for JPEG steganography. Experimental results show that the proposed method outperforms considerably other state-of-the-art JPEG steganographic schemes, i.e., J-UNIWARD and UERD, for the most effective feature set GFR at present, and rivals them for other feature sets, e.g., DCTR and CC-JRM. [ABSTRACT FROM AUTHOR]

At recently, the most of the digital images are stored and transferred in their compressed format using discrete cosine transform (DCT)-based compression technique. DCT is one of the most important data compression technique due to the efforts from Joint Photographic Experts Group(JPEG). Blind steganalysis means how to detect the presence of the messages that are hidden using different types of steganography algorithms and has the ability to detect new unknown steganography algorithms. This paper presents blind steganalysis technique that can reliably detect the existence of messages hidden in JPEG files. In order to save the computation and memory cost, it is desirable to have image processing operations implemented directly in the DCT domain. The proposed method is based on extracting features directly from DCT domain through the analysis of differences between DCT coefficients before and after cropping. The extracted features are prepared as input to Support Vector Machine (SVM) to classify the image as stego (image that contain secret message) or clean (image that does not contain secret message). The experiments performed show that the proposed method yields better classification accuracy compared with other related works. [ABSTRACT FROM AUTHOR]

To improve the detection accuracy for content-adaptive JPEG steganography, which often constrains the embedding changes to complex texture regions, a new steganalysis feature called the GRF (Gabor Rich Feature) based on two-dimensional (2D) Gabor filters is proposed. First, the diverse 2D Gabor filters are generated and used to filter the decompressed JPEG image. Second, five types of statistical features are extracted from the filtered images and these features are merged according to their respective symmetry. Third, all the features are combined and feature selection is performed to reduce dimensionality. Last, an ensemble classifier is used to assemble the steganalysis feature as well as the final steganalyzer. The experimental results show that the proposed steganalysis feature can achieve a performance that is competitive with the state-of-the-art steganalysis features when used for the detection of the latest content-adaptive JPEG steganography algorithms. [ABSTRACT FROM AUTHOR]

JPEG steganography detection is an active research topic in the field of information hiding due to the wide use of JPEG image in social network, image-sharing websites, and Internet communication, etc. In this paper, a new steganalysis method for content-adaptive JPEG steganography is proposed by integrating the evolutionary feature selection and classifier ensemble selection. First, the whole framework of the proposed steganalysis method is presented and then the characteristic of the proposed method is analyzed. Second, the feature selection method based on genetic algorithm is given and the implement process is described in detail. Third, the method of classifier ensemble selection is proposed based on Pareto evolutionary optimization. The experimental results indicate the proposed steganalysis method can achieve a competitive detection performance by compared with the state-of-the-art steganalysis methods when used for the detection of the latest content-adaptive JPEG steganography algorithms. [ABSTRACT FROM AUTHOR]

The current JPEG steganalysis systems have attained outstanding achievements. A considerable variety of strategies for feature extraction are developed. However, a common shortcoming in the traditional image steganalysis techniques is that they are conducted on the entire image and do not take advantage of the content diversity. In this paper, a new steganalysis algorithm based on image segmentation is proposed to enable us to utilize the content characteristics of JPEG images. The images are segmented into several sub-images according to the texture complexity. The steganalysis features of each type of sub-images with the same or close texture complexity are extracted separately to build a classifier. The steganalysis results of the entire image are determined through a weighted fusing process. Experimental results demonstrate that the proposed method exhibits excellent performance and significantly improves the detection accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a risk-sensitive hinge loss function-based cognitive ensemble of extreme learning machine (ELM) classifiers for JPEG steganalysis. ELM is a single hidden-layer feed-forward network that chooses the input parameters randomly and estimates the output weights analytically. For steganalysis, we have extracted 548-dimensional merge features and trained ELM to approximate the functional relationship between the merge features and class label. Further, we use a cognitive ensemble of ELM classifier with risk-sensitive hinge loss function for accurate steganalysis. As the hinge loss error function is shown to be better than mean-squared error function for classification problems, here, the individual ELM classifiers are developed based on hinge loss error function. The cognition in the ensemble of ELM obtains the weighted sum of individual classifiers by enhancing the outputs of winning classifiers for a sample, while penalizing the other classifiers for the sample. Thus, the cognitive ensemble ELM classifier positively exploits the effect of initialization in each classifier to obtain the best results. The performance of the cognitive ensemble ELM in performing the steganalysis is compared to that of a single ELM, and the existing ensemble support vector machine classifier for steganalysis. Performance results show the superior classification ability of the cognitive ensemble ELM classifier. [ABSTRACT FROM AUTHOR]

Quantitative steganalysis strives to estimate the change rate defined as the relative number of embedding changes introduced by steganography. In this paper, we propose two new classes of quantitative steganalysis methods for the steganographic algorithm Jsteg. The first class obtains the change-rate estimate using a maximum likelihood estimator equipped with a precover model. While this approach provides better accuracy than existing structural attacks, it becomes computationally intractable with increasing complexity of the cover model. The second class of methods computes the change-rate estimate by minimizing an objective function constructed from a heuristically formed zero message hypothesis. The advantage of this heuristic approach is a low implementation complexity and modular architecture that allows flexible incorporation of higher order statistics of discrete cosine transform coefficients. The proposed methods are experimentally compared with current state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

Abstract: A high-performance JPEG steganography should be secure enough to resist modern steganalysis. In this paper, we propose a high-performance JPEG steganographic method. The proposed method adopts the complementary embedding strategy to avoid the detections of several statistical attacks. To show the effectiveness of the proposed method, several statistical attacks are simulated and used to detect the stego-images created by the proposed method. Several famous JPEG steganographic algorithms are also simulated for comparisons with the proposed method. Experimental results show that the proposed steganographic method has superior performance both in capacity and security, and is practical for the application of secret communication. [Copyright &y& Elsevier]

• A novel design paradigm of distortion cost function for efficient JPEG steganography is proposed, which incorporates the statistics of both the spatial and DCT domains by a generalized Distortion Cost Domain Transformation (DCDT) function. • The proposed JPEG steganographic distortion cost function is represented in a more general exponential model, aiming to flexibly allocate the embedding data. • The proposed scheme can be easily extended to the mutually dependent embedding with the help of the proposed DCDT function. • Experimental results show that the proposed scheme can outperform other SOTA JPEG steganographic schemes, e.g., UERD, J-UNIWARD and GUED, and rival the BET with an order of magnitude lower computational complexity. • The proposed scheme can not only improve the performance against JPEG phase-aware steganalysis, but also broaden the applications of existing image steganographic schemes in spatial domain. Recently, with the introduction of JPEG (Joint Photographic Experts Group) phase-aware steganalysis features, e.g., GFR, the design of JPEG steganographic distortion cost function turns to maintain not only the statistical undetectability in DCT (Discrete Cosine Transform) domain but also in spatial domain. To tackle this issue, this paper presents a novel paradigm for the design of JPEG steganographic distortion cost function, which calculates the distortion cost via a generalized Distortion Cost Domain Transformation (DCDT) function. The proposed function comprises the decompressed pixel block embedding changes and their corresponding embedding distortion costs for unit change, where the pixel embedding distortion costs are represented in a more general exponential model, aiming to flexibly allocate the embedding data. In this way, the JPEG steganography could be formulated as the optimization problem of minimizing the overall distortion cost in its decompressed spatial domain, which is equivalent to maximizing its statistical undetectability against JPEG phase-aware steganalysis features. Experimental results show that the proposed DCDT equipped with HiLL (a spatial steganographic distortion cost function) is superior to other state-of-the-art JPEG steganographic schemes, e.g., UERD, J-UNIWARD, and GUED in resisting the detection of JPEG phase-aware feature-based steganalyzers GFR and SCA-GFR, and rivals BET-HiLL with one order of magnitude lower computational complexity, along with the possibility of being further improved by considering the mutually dependent embedding interactions. In addition, the proposed DCDT is also verified to be effective for different image databases and quality factors. [ABSTRACT FROM AUTHOR]

• Proposed scheme can resist repetitive compression during network transmission. • Proposed scheme can be used for steganography in uncontrolled network applications. • Adaptive dither adjustment is performed to dynamically modify the coefficients. • Competitive experiments are achieved by social image databases. Existing work on steganography mostly assumes that an image remains unchanged during transmission from the sender to the receiver. This assumption, however, may not hold in the era of the Internet due to unknown compression from the network service providers. As a result, the hidden information cannot be correctly recovered at the receiver. To address the problem, we design a new JPEG steganographic method that can resist repetitive compression during network transmission, without even knowing the compression process controlled by the network service providers. Our method uses a simulated repetitive compression network, and based on its feedback performs adaptive dither adjustment to dynamically modify the DCT coefficients disturbed by the compression process. Stego images generated with our method can be used to successfully extract the original secret messages, even after the stego images pass through multiple unknown compression processes during network transmission. Extensive experiments demonstrate that compared with existing JPEG steganographic methods, our method can effectively resist repetitive compression, while maintaining a lower bit error rate and strong anti-steganalysis capability. [ABSTRACT FROM AUTHOR]