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A comprehensive survey on robust image watermarking

2022; Elsevier BV; Volume: 488; Linguagem: Inglês

10.1016/j.neucom.2022.02.083

1872-8286

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

Video Surveillance and Tracking Methods

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.