Multivariate gaussian model for designing additive distortion for steganography

Currently, the most successful approach to steganography in empirical objects, such as digital media, is to cast the embedding problem as source coding with a fidelity constraint. The sender specifies the costs of changing each cover element and then embeds a given payload by minimizing the total embedding cost. Since efficient practical codes exist that embed near the rate-distortion bound, the remaining task left to the steganographer is the fidelity measure - the choice of the costs. In the past, the costs were obtained either in an ad hoc manner or determined from the effects of embedding in a chosen feature space. In this paper, we adopt a different strategy in which the cover is modeled as a sequence of independent but not necessarily identically distributed quantized Gaussians and the embedding change probabilities are derived to minimize the total KL divergence within the chosen model for a given embedding operation and payload. Despite the simplicity of the adopted model, the resulting stegosystem exhibits security that is comparable to current state-of-the-art methods methods across a wide range of payloads.