On the relationship between embedding costs and steganographic capacity

Contemporary steganography in digital media is dominated by the framework of additive distortion minimization: every possible change is given a cost, and the embedder minimizes total cost using some variant of the Syndrome-Trellis Code algorithm. One can derive the relationship between the cost of each change c_i and the probability that it should be made pi_i, but the literature has not examined the relationship between the costs and the total capacity (secure payload size) of the cover. In this paper we attempt to uncover such a relationship, asymptotically, for a simple independent pixel model of covers. We consider a 'knowing' detector who is aware of the embedding costs, in which case sum pi_i^2 c_i should be optimized. It is shown that the total of the inverse costs, sum c_i^-1, along with the embedder's desired security against an optimal opponent, determines the asymptotic capacity. This result also recovers a Square Root Law. Some simple simulations confirm the relationship between costs and capacity in this ideal model

Steganography by Minimizing Statistical Detectability: The cases of JPEG and Color Images.

International audienceThis short paper presents a novel method for steganography in JPEG-compressed images, extended the so-called MiPOD scheme based on minimizing the detection accuracy of the most-powerful test using a Gaussian model of independent DCT coefficients. This method is also applied to address the problem of embedding into color JPEG images. The main issue in such case is that color channels are not processed in the same way and, hence, a statistically based approach is expected to bring significant improvements when one needs to consider heterogeneous channels together.The results presented show that, on the one hand, the extension of MiPOD for JPEG domain, referred to as J-MiPOD, is very competitive as compared to current state-of-the-art embedding schemes. On the other hands, we also show that addressing the problem of embedding in JPEG color images is far from being straightforward and that future works are required to understand better how to deal with color channels in JPEG images

Simulating suboptimal steganographic embedding

Researchers who wish to benchmark the detectability of steganographic distortion functions typically simulate stego objects. However, the difference (coding loss) between simulated stego objects, and real stego objects is significant, and dependent on multiple factors. In this paper, we first identify some factors affecting the coding loss, then propose a method to estimate and correct for coding loss by sampling a few covers and messages. This allows us to simulate suboptimally-coded stego objects which are more accurate representations of real stego objects. We test our results against real embeddings, and naive PLS simulation, showing our simulated stego objects are closer to real embeddings in terms of both distortion and detectability. This is the case even when only a single image and message as used to estimate the loss