Low complexity template-based watermarking with neural networks and various embedding templates. (September 2022)

• Record Type: Journal Article
• Authors: Dzhanashia, Kristina; Evsutin, Oleg
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Watermarking -- Digital images -- Template-based watermarking -- Robustness -- Low complexity; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108194
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The popularity of watermarking was amplified due to the emergence of new immersive applications that connect the digital and physical worlds such as reading with a smartphone a watermark located in a physical object that leads to an online, dynamic source. Such applications require new watermarking schemes which must be robust enough to withstand significant watermark distortions and have low complexity in the sense that they must be adapted to be used on devices with constrained resources that are common in today's cyber-physical world. The key contribution of this work is the novel robust and low-complex template-based watermarking scheme that uses neural networks. Neural networks have been applied as they have already shown a distinguishable result in such image processing tasks as object detection and image restoration. The experiments show that our scheme outperforms the existing state-of-the-art template-based schemes in terms of robustness without visual degradation.