It is widely recognized that incorporating side-information at the sender can significantly improve steganographic security in practice. Currently, most side-informed schemes for digital images utilize a high quality “precover” image that is subsequently processed and then jointly quantized and embedded with a secret. In this paper, we investigate an alternative form of side-information in the form of two JPEG images of the same scene. The second JPEG image is used to determine the preferred polarity of embedding changes and to modulate their costs. Tests on real imagery show a very significant improvement in empirical security with respect to steganography utilizing a single JPEG image.

It is widely recognized that incorporating side-information at the sender can significantly improve steganographic security in practice. Currently, most side-informed schemes utilize a high-quality “precover” image that is subsequently processed and then jointly quantized and embedded with a secret. In this paper, we investigate an alternative form of side-information-a set of multiple JPEG images of the same scene-for applications when the sender does not have access to a precover. The additional JPEG images are used to determine the preferred polarity of embedding changes to modulate the costs of changing individual DCT coefficients in an existing embedding scheme. Tests on real images with synthesized acquisition noise and on real multiple acquisitions obtained with a tripod-mounted and hand-held digital camera show a rather significant improvement in empirical security with respect to steganography utilizing a single JPEG image. The proposed empirically determined modulation of embedding costs is justified using Monte-Carlo simulations by showing that qualitatively the same modulation minimizes the Bhattacharyya distance between a quantized generalized Gaussian model of cover and stego DCT coefficients corrupted by AWG acquisition noise.

All the modern steganographic algorithms for digital images are content adaptive in the sense that they restrict the embedding modifications to complex regions of the cover, which are difficult to model for the steganalyst. The probabilities with which the individual cover elements are modified (the selection channel) are jointly determined by the size of the embedded payload and the content complexity. The most accurate detection of content-adaptive steganography is currently achieved with the detectors built as classifiers trained on cover and stego features that incorporate the knowledge of the selection channel. While the selection-channel-aware features have been proposed for detection of spatial domain steganography, an equivalent for the JPEG domain does not exist. Since modern steganographic algorithms for JPEG images are currently best detected with the features formed by the histograms of the noise residuals split by their JPEG phase, we use such feature sets as a starting point in this paper and extend their design to incorporate the knowledge of the selection channel. This is achieved by accumulating in the histograms a quantity that bounds the expected absolute distortion of the residual. The proposed features can be efficiently computed and provide a substantial detection gain across all the tested algorithms especially for small payloads.

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.

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.