Estimating the Information Theoretic Optimal Stego Noise.

We recently developed a new benchmark for steganography, underpinned by the square root law of capacity, called Steganographic Fisher Information (SFI). It is related to the multiplicative constant for the square root capacity rate and represents a truly information theoretic measure of asymptotic evidence. Given a very large corpus of covers from which the joint histograms can be estimated, an estimator for SFI was derived in [1], and certain aspects of embedding and detection were compared using this benchmark. In this paper we concentrate on the evidence presented by various spatial-domain embedding operations. We extend the technology of [1] in two ways, to convex combinations of arbitrary so-called independent embedding functions. We then apply the new techniques to estimate, in genuine sets of cover images, the spatial-domain stego noise shape which optimally trades evidence - in terms of asymptotic KL divergence - for capacity. The results suggest that smallest embedding changes are optimal for cover images not exhibiting much noise, and also for cover images with significant saturation, but in noisy images it is superior to embed with more stego noise in fewer locations. © 2009 Springer

Estimating the Information Theoretic Optimal Stego Noise.

We recently developed a new benchmark for steganography, underpinned by the square root law of capacity, called Steganographic Fisher Information (SFI). It is related to the multiplicative constant for the square root capacity rate and represents a truly information theoretic measure of asymptotic evidence. Given a very large corpus of covers from which the joint histograms can be estimated, an estimator for SFI was derived in [1], and certain aspects of embedding and detection were compared using this benchmark. In this paper we concentrate on the evidence presented by various spatial-domain embedding operations. We extend the technology of [1] in two ways, to convex combinations of arbitrary so-called independent embedding functions. We then apply the new techniques to estimate, in genuine sets of cover images, the spatial-domain stego noise shape which optimally trades evidence - in terms of asymptotic KL divergence - for capacity. The results suggest that smallest embedding changes are optimal for cover images not exhibiting much noise, and also for cover images with significant saturation, but in noisy images it is superior to embed with more stego noise in fewer locations. © 2009 Springer

The role of side information in steganography

Das Ziel digitaler Steganographie ist es, eine geheime Kommunikation in digitalen Medien zu verstecken. Der übliche Ansatz ist es, die Nachricht in einem empirischen Trägermedium zu verstecken. In dieser Arbeit definieren wir den Begriff der Steganographischen Seiteninformation (SSI). Diese Definition umfasst alle wichtigen Eigenschaften von SSI. Wir begründen die Definition informationstheoretisch und erklären den Einsatz von SSI. Alle neueren steganographischen Algorithmen nutzen SSI um die Nachricht einzubetten. Wir entwickeln einen Angriff auf adaptive Steganographie und zeigen anhand von weit verbreiteten SSI-Varianten, dass unser Angriff funktioniert. Wir folgern, dass adaptive Steganographie spieltheoretisch beschrieben werden muss. Wir entwickeln ein spieltheoretisches Modell für solch ein System und berechnen die spieltheoretisch optimalen Strategien. Wir schlussfolgern, dass ein Steganograph diesen Strategien folgen sollte. Zudem entwickeln wir eine neue spieltheoretisch optimale Strategie zur Einbettung, die sogenannten Ausgleichseinbettungsstrategien.The  goal of digital steganography is to hide a secret communication in digital media. The common approach in steganography is to hide the secret messages in empirical cover objects. We are the first to define Steganographic Side Information (SSI). Our definition of SSI captures all relevant properties of SSI. We explain the common usage of SSI. All recent steganographic schemes use SSI to identify suitable areas fot the embedding change. We develop a targeted attack on four widely used variants of SSI, and show that our attack detects them almost perfectly. We argue that the steganographic competition must be framed with means of game theory. We present a game-theoretical framework that captures all relevant properties of such a steganographic system. We instantiate the framework with five different models and solve each of these models for game-theoretically optimal strategies. Inspired by our solutions, we give a new paradigm for secure adaptive steganography, the so-called equalizer embedding strategies