Abstract: Recently, the application of deep learning in the field of image watermarking has gained increasing attention. Image watermarking is a technology used to protect the copyright and content integrity of images against piracy and tampering by embedding recognizable identifying information in the image. Deep learning models enable efficient image watermark embedding and extraction by automatically learning the features and structure of image. This paper summarizes the research work related to deep learning image watermarking in recent years. Firstly, this paper introduces the basic framework and design requirements of deep learning-based image watermarking. Subsequently, this paper categorizes and provides detailed descriptions of different modules within the watermarking framework. Then, this paper summarizes and compares the robustness performance of various deep watermarking models. Finally, this paper analyzes the future development prospects of deep learning-based image watermarking.
External IDs:doi:10.1145/3638884.3638905
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