SaTC: CORE: Medium: Analytic versus Data Driven Models in Steganography: Empowering Each Other

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Steganography is a secret communication method that achieves privacy by hiding the actual message in some other innocuous looking object. Digital media, such as images, are ideal for this purpose because they can be slightly modified to encode a secret without making these modifications visible to a human or detectable by a computer. Steganography thus offers privacy to citizens in countries that censor communication channels or prohibit the usage of encryption. Modern steganography and the detection of its use heavily rely on machine learning and artificial intelligence. While these "black box" systems offer superior performance to existing analytical methods, comparatively little is known about the performance limits of such tools and even less about how and why these systems work. This project aims at addressing these deficiencies by combining conventional analytical modeling tools with modern machine learning in order to obtain insight into how modern artificial intelligence systems work in detecting steganography, establish their limits, and discover new fundamental knowledge about steganography in digital media. The key idea of the project is to combine the explanatory power of conventional analytic modeling tools with the flexibility of machine learning, leveraging their complementary strengths to address some of the most fundamental open problems in this field. Artificial but realistic image datasets will be used to compute limits and assess optimality of modern data-driven steganographic algorithms and steganalysis detectors. Machine learning models suitably restricted in terms of the task being learned, the form of the training examples, and the learnable architecture will be used for knowledge discovery and for building novel analytic models relevant to practitioners. Together, these will lead to deeper understanding of the limits of practical covert systems and their detection. This will facilitate development of better counter-deception methods and defenses against steganography when used by terrorists, criminals, and spies for planning and coordination of disruptive activities. This, in turn, will advance both the research frontier and the practical impact of steganography, while methods based on the research may be useful for related fields including digital forensics and data provenance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.