Currently, the most successful approach to steganography in empirical objects, such as digital media, is to embed the payload while minimizing a suitably defined distortion function. The design of the distortion is essentially the only task left to the steganographer since efficient practical codes exist that embed near the payload-distortion bound. The practitioner’s goal is to design the distortion to obtain a scheme with a high empirical statistical detectability. In this paper, we propose a universal distortion design called universal wavelet relative distortion (UNIWARD) that can be applied for embedding in an arbitrary domain. The embedding distortion is computed as a sum of relative changes of coefficients in a directional filter bank decomposition of the cover image. The directionality forces the embedding changes to such parts of the cover object that are difficult to model in multiple directions, such as textures or noisy regions, while avoiding smooth regions or clean edges. We demonstrate experimentally using rich models as well as targeted attacks that steganographic methods built using UNIWARD match or outperform the current state of the art in the spatial domain, JPEG domain, and side-informed JPEG domain.

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Universal distortion function for steganography in an arbitrary domain

Steganography detectors built as deep convolutional neural networks have firmly established themselves as superior to the previous detection paradigm – classifiers based on rich media models. Existing network architectures, however, still contain elements designed by hand, such as fixed or constrained convolutional kernels, heuristic initialization of kernels, the thresholded linear unit that mimics truncation in rich models, quantization of feature maps, and awareness of JPEG phase. In this work, we describe a deep residual architecture designed to minimize the use of heuristics and externally enforced elements that is universal in the sense that it provides state-of-the-art detection accuracy for both spatial-domain and JPEG steganography. The key part of the proposed architecture is a significantly expanded front part of the detector that “computes noise residuals” in which pooling has been disabled to prevent suppression of the stego signal. Extensive experiments show the superior performance of this network with a significant improvement, especially in the JPEG domain. Further performance boost is observed by supplying the selection channel as a second channel.

Deep Residual Network for Steganalysis of Digital Images

This paper introduces a novel feature set for steganalysis of JPEG images. The features are engineered as first-order statistics of quantized noise residuals obtained from the decompressed JPEG image using 64 kernels of the discrete cosine transform (DCT) (the so-called undecimated DCT). This approach can be interpreted as a projection model in the JPEG domain, forming thus a counterpart to the projection spatial rich model. The most appealing aspect of this proposed steganalysis feature set is its low computational complexity, lower dimensionality in comparison with other rich models, and a competitive performance with respect to previously proposed JPEG domain steganalysis features.

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Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT

Currently, the most secure practical steganographic schemes for empirical cover sources embed their payload while minimizing a distortion function designed to capture statistical detectability. Since there exists a general framework for this embedding paradigm with established payload-distortion bounds as well as near-optimal practical coding schemes, building an embedding scheme has been essentially reduced to the distortion design. This is not an easy task as relating distortion to statistical detectability is a hard and open problem. In this article, we propose an innovative idea to measure the embedding distortion in one fixed domain independently of the domain where the embedding changes (and coding) are carried out. The proposed universal distortion is additive and evaluates the cost of changing an image element (e.g., pixel or DCT coefficient) from directional residuals obtained using a Daubechies wavelet filter bank. The intuition is to limit the embedding changes only to those parts of the cover that are difficult to model in multiple directions while avoiding smooth regions and clean edges. The utility of the universal distortion is demonstrated by constructing steganographic schemes in the spatial, JPEG, and side-informed JPEG domains, and comparing their security to current state-of-the-art methods using classifiers trained with rich media models.

/pdf/digital-image-steganography-using-universal-distortion-1jlskgdftz.pdf

Digital image steganography using universal distortion

Adaptive JPEG steganographic schemes are difficult to preserve the image texture features in all scales and orientations when the embedding changes are constrained to the complicated texture regions, then a steganalysis feature extraction method is proposed based on 2 dimensional (2D) Gabor filters. The 2D Gabor filters have certain optimal joint localization properties in the spatial domain and in the spatial frequency domain. They can describe the image texture features from different scales and orientations, therefore the changes of image statistical characteristics caused by steganography embedding can be captured more effectively. For the proposed feature extraction method, the decompressed JPEG image is filtered by 2D Gabor filters with different scales and orientations firstly. Then, the histogram features are extracted from all the filtered images.Lastly, the ensemble classifier is used to assemble the proposed steganalysis feature as well as the final steganalyzer. The experimental results show that the proposed steganalysis feature can achieve a competitive performance by comparing with the other steganalysis features when they are used for the detection performance of adaptive JPEG steganography such as UED, JUNIWARD and SI-UNIWARD.

Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters

Steganography is the science and art of covert communication, which aims to hide the secret messages into a cover medium while achieving the least possible statistical detectability. To this end, the framework of minimal distortion embedding is widely adopted in the development of the steganographic system, in which a well designed distortion function is of vital importance. In this paper, a class of new distortion functions known as uniform embedding distortion function (UED) is presented for both side-informed and non side-informed secure JPEG steganography. By incorporating the syndrome trellis coding, the best codeword with minimal distortion for a given message is determined with UED, which, instead of random modification, tries to spread the embedding modification uniformly to quantized discrete cosine transform (DCT) coefficients of all possible magnitudes. In this way, less statistical detectability is achieved, owing to the reduction of the average changes of the first- and second-order statistics for DCT coefficients as a whole. The effectiveness of the proposed scheme is verified with evidence obtained from exhaustive experiments using popular steganalyzers with various feature sets on the BOSSbase database. Compared with prior arts, the proposed scheme gains favorable performance in terms of secure embedding capacity against steganalysis.

Uniform Embedding for Efficient JPEG Steganography

Uniform embedding was first introduced in 2012 for non-side-informed JPEG steganography, and then extended to the side-informed JPEG steganography in 2014. The idea behind uniform embedding is that, by uniformly spreading the embedding modifications to the quantized discrete cosine transform (DCT) coefficients of all possible magnitudes, the average changes of the first-order and the second-order statistics can be possibly minimized, which leads to less statistical detectability. The purpose of this paper is to refine the uniform embedding by considering the relative changes of statistical model for digital images, aiming to make the embedding modifications to be proportional to the coefficient of variation. Such a new strategy can be regarded as generalized uniform embedding in substantial sense. Compared with the original uniform embedding distortion (UED), the proposed method uses all the DCT coefficients (including the DC, zero, and non-zero AC coefficients) as the cover elements. We call the corresponding distortion function uniform embedding revisited distortion (UERD), which incorporates the complexities of both the DCT block and the DCT mode of each DCT coefficient (i.e., selection channel), and can be directly derived from the DCT domain. The effectiveness of the proposed scheme is verified with the evidence obtained from the exhaustive experiments using a popular steganalyzer with rich models on the BOSSbase database. The proposed UERD gains a significant performance improvement in terms of secure embedding capacity when compared with the original UED, and rivals the current state-of-the-art with much reduced computational complexity.

Using Statistical Image Model for JPEG Steganography: Uniform Embedding Revisited

Steganography is the science and art of covert communication Its objective is to hide the most secret messages into a cover object with the least possible statistical detectability In practice, this is generally realized by a framework of minimal distortion embedding This paper presents an efficient JPEG steganographic scheme based on syndrome trellis coding (STC) and a uniform embedding strategy, which, instead of random modification, tries to modify nonzero quantized DCT coefficients of different magnitude with equal probability, leading to possible minimal artifacts for statistics of DCT coefficients as a whole The distortion metric corresponding to the uniform embedding is based on the magnitude of the DCT coefficients and both their intra- and inter-block neighborhood coefficients and known as uniform embedding distortion metric (UED) With the proposed scheme, the STC provides multiple codewords for a given message, while the UED determines the best one with minimal distortion In this way, the average statistics change in each bin is significantly reduced, which corresponds to less detectability of steganalysis Compared with prior arts, experimental results demonstrate the superior performance of the proposed scheme in terms of secure embedding capacity against steganalysis

Linjie Guo

Papers

Uniform Embedding for Efficient JPEG Steganography

Using Statistical Image Model for JPEG Steganography: Uniform Embedding Revisited

An efficient JPEG steganographic scheme using uniform embedding