Watermarking Techniques Based on Deep Learning Approaches: A Review

Digital watermarking plays a vital role in protecting the rights and integrity of images, preventing unauthorized use, and promoting a safer and more accountable digital environment. Conventional watermarking schemes often suffer from limited robustness and susceptibility to various image processing operations, while deep learning-based watermarking schemes provide enhanced resilience to attacks and greater adaptability to complex image transformations, making them more effective in maintaining the integrity and ownership of digital content. This paper provides a concise analysis of commonly utilized deep learning networks and watermarking methods. The article begins by discussing the general framework, applications, and classification of watermarking based on different use cases. It then delves into deep learning network models such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Deep Neural Networks (DNN), along with associated watermarking techniques. Lastly, the paper addresses the challenges and offers guidance for future developers and researchers in this field