Journal
CONNECTION SCIENCE
Volume 34, Issue 1, Pages 1719-1735Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/09540091.2022.2078792
Keywords
Automatic coding; mathematical optimisation; reversible steganography
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Authentication mechanisms, including steganography, are crucial in defending against cybercrime. Reversible steganography has been developed to address fidelity-sensitive situations. Predictive analytics and reversible steganographic coding play vital roles in this field. While existing coding methods rely on heuristics and machine learning, this study focuses on achieving optimal coding through mathematical optimization.
Authentication mechanisms are at the forefront of defending the world from various types of cybercrime. Steganography can serve as an authentication solution through the use of a digital signature embedded in a carrier object to ensure the integrity of the object and simultaneously lighten the burden of metadata management. Nevertheless, despite being generally imperceptible to human sensory systems, any degree of steganographic distortion might be inadmissible in fidelity-sensitive situations such as forensic science, legal proceedings, medical diagnosis and military reconnaissance. This has led to the development of reversible steganography. A fundamental element of reversible steganography is predictive analytics, for which powerful neural network models have been effectively deployed. Another core element is reversible steganographic coding. Contemporary coding is based primarily on heuristics, which offers a shortcut towards sufficient, but not necessarily optimal, capacity-distortion performance. While attempts have been made to realise automatic coding with neural networks, perfect reversibility is unattainable via such learning machinery. Instead of relying on heuristics and machine learning, we aim to derive optimal coding by means of mathematical optimisation. In this study, we formulate reversible steganographic coding as a nonlinear discrete optimisation problem with a logarithmic capacity constraint and a quadratic distortion objective. Linearisation techniques are developed to enable iterative mixed-integer linear programming. Experimental results validate the near-optimality of the proposed optimisation algorithm when benchmarked against a brute-force method.
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