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A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking

Applied sciences, 2023-10, Vol.13 (21), p.11852 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2076-3417 ;EISSN: 2076-3417 ;DOI: 10.3390/app132111852

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  • Title:
    A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
  • Author: Zhong, Xin ; Das, Arjon ; Alrasheedi, Fahad ; Tanvir, Abdullah
  • Subjects: Adaptability ; Algorithms ; Communication ; Computational linguistics ; Deep learning ; Digital media ; image watermarking ; Innovations ; Intellectual property ; Internet of Things ; Language processing ; Medical imaging equipment ; Natural language interfaces ; survey ; Surveys
  • Is Part Of: Applied sciences, 2023-10, Vol.13 (21), p.11852
  • Description: This paper presents a comprehensive survey of deep learning-based image watermarking; this technique entails the invisible embedding and extraction of watermarks within a cover image, aiming for a seamless combination of robustness and adaptability. We navigate the complex landscape of this interdisciplinary domain, linking historical foundations, current innovations, and prospective developments. Unlike existing literature, our study concentrates exclusively on image watermarking with deep learning, delivering an in-depth, yet brief analysis enriched by three fundamental contributions. First, we introduce a refined categorization, segmenting the field into embedder–extractor, deep networks for feature transformation, and hybrid methods. This taxonomy, inspired by the varied roles of deep learning across studies, is designed to infuse clarity, offering readers technical insights and directional guidance. Second, our exploration dives into representative methodologies, encapsulating the diverse research directions and inherent challenges within each category to provide a consolidated perspective. Lastly, we venture beyond established boundaries, outlining emerging frontiers and providing detailed insights into prospective research avenues.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2076-3417
    EISSN: 2076-3417
    DOI: 10.3390/app132111852
  • Source: ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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