Exploring Watermarking Strategies in Deep Learning: A Comprehensive Review - Mohsen Ibrahim, Hadeel; Talib Gaata, Methaq; Abdulaali Abdulbaqi, Huda (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2025)
Recently, there has been a notable increase in the need for the generation, dissemination, and storage of extensive volumes of multimedia data, especially digital images, generated by different intelligent devices and sensors. Along with other security vulnerabilities, such activity results in unauthorized access and false use of data. Embedding a watermark design into a digital cover and then removing it helps to address ownership conflicts and copyright infringement concerns related to the media data. Deep-learning methods are currently rather helpful for watermarking because of their high accuracy, great precision, and strong learning ability. This article presents a comprehensive examination of watermarking techniques used in deep learning contexts. We start by explaining the basic ideas of both traditional and learning-based digital watermarking, and then we look at common watermarking methods that use deep learning models. We then provide a succinct summary and comparison of the most recent contributions in the literature. Finally, we highlight the challenges related to obfuscation and suggest avenues for further study.