Open Access

REVIEW

Robust Deep Image Watermarking: A Survey

Yuanjing Luo, Xichen Tan, Zhiping Cai^*

College of Computer, National University of Defense Technology, Changsha, 410005, China

* Corresponding Author: Zhiping Cai. Email:

Computers, Materials & Continua 2024, 81(1), 133-160. https://doi.org/10.32604/cmc.2024.055150

Received 19 June 2024; Accepted 03 September 2024; Issue published 15 October 2024

Abstract

In the era of internet proliferation, safeguarding digital media copyright and integrity, especially for images, is imperative. Digital watermarking stands out as a pivotal solution for image security. With the advent of deep learning, watermarking has seen significant advancements. Our review focuses on the innovative deep watermarking approaches that employ neural networks to identify robust embedding spaces, resilient to various attacks. These methods, characterized by a streamlined encoder-decoder architecture, have shown enhanced performance through the incorporation of novel training modules. This article offers an in-depth analysis of deep watermarking’s core technologies, current status, and prospective trajectories, evaluating recent scholarly contributions across diverse frameworks. It concludes with an overview of the technical hurdles and prospects, providing essential insights for ongoing and future research endeavors in digital image watermarking.

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Keywords

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