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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(More)
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(More)
Today, the most reliable detectors of steganography in empirical cover sources, such as digital images coming from a known source, are built using machine-learning by representing images with joint distributions (co-occurrences) of neighboring noise residual samples computed using local pixel predictors. In this paper, we propose an alternative statistical(More)
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(More)
Recently, a new steganographic method was introduced that utilizes a universal distortion function called UNI-WARD. The distortion between the cover and stego image is computed as a sum of relative changes of wavelet coefficients representing both images. As already pointed out in the original publication, the selection channel of the spatial version of(More)
The FLD ensemble classifier is a widely used machine learning tool for steganalysis of digital media due to its efficiency when working with high dimensional feature sets. This paper explains how this classifier can be formulated within the framework of optimal detection by using an accurate statistical model of base learners' projections and the hypothesis(More)
This paper is an attempt to analyze the interaction between Alice and Warden in Steganography using the Game Theory. We focus on the modern steganographic embedding paradigm based on minimizing an additive distortion function. The strategies of both players comprise of the probabilistic selection channel. The Warden is granted the knowledge of the payload(More)
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(More)
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(More)
Currently, the best detectors of content-adaptive steganography are built as classifiers trained on examples of cover and stego images represented with rich media models (features) formed by histograms (or co-occurrences) of quantized noise residuals. Recently, it has been shown that adaptive steganography can be more accurately detected by incorporating(More)