A comprehensive survey on robust image watermarking

Wenbo Wan, Jun Wang, Yunming Zhang, Jing Li, Hui Yu*, Jiande Sun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

With the rapid development and popularity of the Internet, multimedia security has become a general essential concern. Especially, as manipulation of digital images gets much easier, the challenges it brings to authentication certification are increasing. As part of the solution, digital watermarking has made significant contributions to image content security and has attracted increasing attention. In this paper, we present a comprehensive review on digital image watermarking methods that were published in recent years illustrating the conventional schemes in different domains. We provide an overview of geometric invariant techniques and emerging watermarking methods for novel medias, such as depth image based rendering (DIBR), high dynamic range (HDR), screen content images (SCIs), and point cloud model. Particularly, as deep learning has achieved a great success in the field of image processing, and has also successfully been used in the field of digital watermarking, learning-based watermarking methods using various neural networks are summarized according to the utilization of neural networks in the single stage training (SST) and double stage training (DST). Finally, we provide an analysis and summary on those methods, and suggest some future research directions.

Original languageEnglish
Pages (from-to)226-247
Number of pages22
JournalNeurocomputing
Volume488
Early online date10 Mar 2022
DOIs
Publication statusEarly online - 10 Mar 2022

Keywords

  • deep learning
  • HDR image
  • image watermarking
  • model watermarking
  • robustness

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