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.

From the perspective of signal detection theory, it seems obvious that knowing the probabilities with which the individual cover elements are modified during message embedding (the so-called probabilistic selection channel) should improve steganalysis. It is, however, not clear how to incorporate this information into steganalysis features when the detector is built as a classifier. In this paper, we propose a variant of the popular spatial rich model (SRM) that makes use of the selection channel. We demonstrate on three state-of-theart content-adaptive steganographic schemes that even an imprecise knowledge of the embedding probabilities can substantially increase the detection accuracy in comparison with feature sets that do not consider the selection channel. Overly adaptive embedding schemes seem to be more vulnerable than schemes that spread the embedding changes more evenly throughout the cover.

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This paper describes a general method for increasing the security of additive steganographic schemes for digital images represented in the spatial domain. Additive embedding schemes first assign costs to individual pixels and then embed the desired payload by minimizing the sum of costs of all changed pixels. The proposed framework can be applied to any such scheme -it starts with the cost assignment and forms a non-additive distortion function that forces adjacent embedding changes to synchronize. Since the distortion function is purposely designed as a sum of locally supported potentials, one can use the Gibbs construction to realize the embedding in practice. The beneficial impact of synchronizing the embedding changes is linked to the fact that modern steganalysis detectors use higher-order statistics of noise residuals obtained by filters with sign-changing kernels and to the fundamental difficulty of accurately estimating the selection channel of a non-additive embedding scheme implemented with several Gibbs sweeps. Both decrease the accuracy of detectors built using rich media models, including their selection-channel-aware versions.

Side-informed steganography is a term used for embedding secret messages while utilizing a higher quality form of the cover object called the precover. The embedding algorithm typically makes use of the quantization errors available when converting the precover to a lower quality cover object. Virtually all previously proposed side-informed steganographic schemes were limited to the case when the side-information is in the form of an uncompressed image and the embedding uses the unquantized DCT coefficients to improve the security when JPEG compressing the precover. Inspired by the side-informed (SI) UNIWARD embedding scheme, in this paper we describe a general principle for incorporating the sideinformation in any steganographic scheme designed to minimize embedding distortion. Further improvement in security is obtained by allowing a ternary embedding operation instead of binary and computing the costs from the unquantized cover. The usefulness of the proposed embedding paradigm is demonstrated on a wide spectrum of various information-reducing image processing operations, including image downsampling, color depth reduction, and filtering. Side-information appears to improve empirical security of existing embedding schemes by a rather large margin.

It is widely recognized that steganography with sideinformation in the form of a precover at the sender enjoys significantly higher empirical security than other embedding schemes. Despite the success of side-informed steganography, current designs are purely heuristic and little has been done to develop the embedding rule from first principles. Building upon the recently proposed MiPOD steganography, in this paper we impose multivariate Gaussian model on acquisition noise and estimate its parameters from the available precover. The embedding is then designed to minimize the KL divergence between cover and stego distributions. In contrast to existing heuristic algorithms that modulate the embedding costs by 1-2|e|, where e is the rounding error, in our model-based approach the sender should modulate the steganographic Fisher information, which is a loose equivalent of embedding costs, by (1-2|e|)^2. Experiments with uncompressed and JPEG images show promise of this theoretically well-founded approach.

In natural steganography, the secret message is hidden by adding to the cover image a noise signal that mimics the heteroscedastic noise introduced naturally during acquisition. The method requires the cover image to be available in its RAW form (the sensor capture). To bring this idea closer to a practical embedding method, in this paper we embed the message in quantized DCT coefficients of a JPEG file by adding independent realizations of the heteroscedastic noise to pixels to make the embedding resemble the same cover image acquired at a larger sensor ISO setting (the so-called cover source switch). To demonstrate the feasibility and practicality of the proposed method and to validate our simplifying assumptions, we work with two digital cameras, one using a monochrome sensor and a second one equipped with a color sensor. We then explore several versions of the embedding algorithm depending on the model of the added noise in the DCT domain and the possible use of demosaicking to convert the raw image values. These experiments indicate that the demosaicking step has a significant impact on statistical detectability for high JPEG quality factors when making independent embedding changes to DCT coefficients. Additionally, for monochrome sensors or low JPEG quality factors very large payload can be embedded with high empirical security.

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