Deep Learning-Optimized Adaptive Image Watermarking Technology and Digital Copyright Protection Integration*
Mingyang Li, Feng Liu · 2025
With the widespread dissemination of digital media, digital copyright protection faces severe challenges. Traditional image watermarking techniques fall short in terms of invisibility, robustness, and security, making it difficult to withstand complex attacks. This paper proposes an integrated approach that combines deep learning with adaptive watermarking. It utilizes convolutional neural networks (CNN) to extract image features, enabling dynamic adjustment of watermark strength. Additionally, encryption and digital signature mechanisms are incorporated to enhance security and tamper-resistance. The proposed solution covers modules for watermark embedding, model training, and copyright validation. Experimental results demonstrate that the proposed method maintains high image quality while achieving excellent extraction accuracy and resistance to interference. The signature mechanism can also accurately detect tampering, thereby strengthening copyright traceability and verification. This research provides a feasible technical pathway for integrating image watermarking with copyright protection.