Sim-to-Real: An Unsupervised Noise Layer for Screen-Camera Watermarking Robustness

The rise of unauthorized screen capturing poses severe security threats, including data leakage and information theft, prompting the need for robust watermarking methods to protect screen-camera (SC) images. Traditional techniques often rely on heuristic mathematical modeling or supervised neural networks, both of which face limitations such as biased approximations and the necessity for paired training data. To overcome these challenges, researchers have introduced the Simulation-to-Real (S2R) method, which utilizes an unsupervised noise layer. This innovative approach learns the discrepancies between modeled simulated noise and actual SC noise distributions without requiring paired data. By effectively capturing the complexities of real-world noise, S2R aims to significantly enhance the robustness of watermarking techniques, thereby improving copyright tracking and protection for digital content. This advancement is crucial in an era where the integrity of digital media is increasing…