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Published on 26 November 2024
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Li,X. (2024). Advanced Reversible Data Hiding Techniques in JPEG Images: Methods, Applications, and Future Perspectives. Applied and Computational Engineering,94,77-85.
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Advanced Reversible Data Hiding Techniques in JPEG Images: Methods, Applications, and Future Perspectives

Xinyi Li *,1,
  • 1 School of Computer Engineering and Science, Shanghai University, Shanghai, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/94/2024MELB0059

Abstract

This paper provides an extensive review of recent advancements in reversible information hiding methods tailored for JPEG images, a predominant compression standard crucial for data security and digital copyright protection. It starts by outlining fundamental concepts related to JPEG image structure and reversible data hiding techniques. The analysis then delves into three primary methods: modifications to JPEG quantization tables, adjustments in Huffman tables, and corrections of Discrete Cosine Transform (DCT) coefficients, detailing how each technique contributes to effective and secure data embedding. Furthermore, the exploration of potential applications in critical areas highlights the versatile utility of these techniques in safeguarding digital content. Concluding with a discussion on future research directions, this paper emphasizes the significant potential for growth in this field, particularly in enhancing security measures and improving efficiency. Such advancements promise to extend the applicability of reversible data hiding, ensuring robust copyright protection and information security in the digital age.

Keywords

Reversible data hiding, quantization table modification, dct coefficient correction

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Cite this article

Li,X. (2024). Advanced Reversible Data Hiding Techniques in JPEG Images: Methods, Applications, and Future Perspectives. Applied and Computational Engineering,94,77-85.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-MLA 2024 Workshop: Securing the Future: Empowering Cyber Defense with Machine Learning and Deep Learning

Conference website: https://2024.confmla.org/
ISBN:978-1-83558-633-4(Print) / 978-1-83558-634-1(Online)
Conference date: 21 November 2024
Editor:Mustafa ISTANBULLU, Ansam Khraisat
Series: Applied and Computational Engineering
Volume number: Vol.94
ISSN:2755-2721(Print) / 2755-273X(Online)

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