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Automation of reversible steganographic coding with nonlinear discrete optimisation
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Chang, Ching-Chun (2022) Automation of reversible steganographic coding with nonlinear discrete optimisation. Connection Science, 34 (1). pp. 1719-1735. doi:10.1080/09540091.2022.2078792 ISSN 0954-0091.
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Official URL: https://doi.org/10.1080/09540091.2022.2078792
Abstract
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.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Data structures (Computer science), Programming languages (Electronic computers), Mathematical optimization , Mathematical optimization -- Computer programs, Cryptography , Artificial intelligence , Human-computer interaction | ||||||
Journal or Publication Title: | Connection Science | ||||||
Publisher: | Taylor and Francis Group | ||||||
ISSN: | 0954-0091 | ||||||
Official Date: | 14 June 2022 | ||||||
Dates: |
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Volume: | 34 | ||||||
Number: | 1 | ||||||
Page Range: | pp. 1719-1735 | ||||||
DOI: | 10.1080/09540091.2022.2078792 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 22 November 2022 | ||||||
Date of first compliant Open Access: | 22 November 2022 |
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