Recent advances in content-adaptive Audio Steganography in Temporal Domain (ASTD) suggest that modification of micro-amplitude samples may compromise its security. To prevent the micro-amplitude samples from being modified, a targeted Large Amplitude First (LAF) rule was adopted in some audio steganographic schemes, e.g., DFR. However, it is observed that the results with LAF rule are often unstable across different datasets, we thus propose a new Micro-Amplitude Suppression (MAS) rule in this paper following the design philosophy of wet paper coding. Unlike DFR where the audio steganographic performance heavily depends on the adopted heuristic filters, we propose to evaluate the embedding cost of cover audio with the Generalized Audio Intrinsic Energy (GAIE), which is obtained by calculating the weighted sum of squared DCT coefficients for each segmented audio clip with carefully designed weights. Extensive experimental results demonstrate that the proposed MAS rule tends to be more general and consistent than the LAF rule, and the proposed GAIE also shows better empirical security performance and audio quality compared to the advanced AAC and DFR_res (a variant of DFR). In addition, by preventing the micro-amplitude samples from being modified, the proposed GAIE_MAS can not only outperform other hand-crafted audio steganographic schemes but also the recently emerged deep learning-based schemes, e.g., IAA.

Conventional image steganography is assumed to transmit the message, in the most securest way possible for a given payload, over lossless channels, and the associated steganographic schemes are generally vulnerable to active attacks, e.g., JPEG re-compression, and scaling, as seen on social networks. Although considerable progress has been made on robust steganography against JPEG re-compression, there exist few steganographic schemes capable of resisting scaling attacks due to the tricky inverse interpolations involved in algorithm design. To tackle this issue, a framework for robust image steganography resisting scaling with general interpolations either in std form with fixed interpolation block, or pre-filtering-based anti-aliasing implementation with variable block, is proposed in this paper. And the task of robust steganography can be formulated as one of constrained integer programming aiming at perfectly recovering the secret message from the stego image while minimizing the difference between cover and stego images and the embedding distortion between scaled cover and scaled stego images. By introducing a metric - the degree of pixel involvement (dPI) to identify the modifiable pixels in the cover image, the optimization problem above could be effectively solved using the branch and bound algorithm (B&B). Extensive experiments demonstrate that the proposed scheme could not only resist scaling attacks with various interpolation techniques at arbitrary scaling factors (SFs), but also outperform the prior art in terms of security between the cover and stego images by a clear margin. In addition, the application of the proposed method in LinkedIn against the joint attacks of scaling and JPEG re-compression also shows its effectiveness on social network in real-world scenarios.

Recent advances on adaptive steganography show that the performance of image steganographic communication can be improved by incorporating the non-additive models that capture the dependencies among adjacent pixels. In this paper, a Gaussian Markov Random Field model (GMRF) with four-element cross neighborhood is proposed to characterize the interactions among local elements of cover images, and the problem of secure image steganography is formulated as the one of minimization of KL-divergence in terms of a series of low-dimensional clique structures associated with GMRF by taking advantages of the conditional independence of GMRF. The adoption of the proposed GMRF tessellates the cover image into two disjoint subimages, and an alternating iterative optimization scheme is developed to effectively embed the given payload while minimizing the total KL-divergence between cover and stego, i.e., the statistical detectability. Experimental results demonstrate that the proposed GMRF outperforms the prior arts of model based schemes, e.g., MiPOD, and rivals the state-of-the-art HiLL for practical steganography, where the selection channel knowledges are unavailable to steganalyzers.

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.

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.