Improving Robustness of Screen-Camera Resilient Watermarking: A Large-Scale Dataset and a Noise Simulation Network

Although screen-camera resilient watermarking addresses issues such as privacy leakage and copyright infringement in digital images to some extent during screen-camera communication. However, in screen-camera scenarios, uncontrolled shooting environments, various display devices, and different lens types introduce more complex noise into the watermarked images. Because some noise generated during the screen-camera process cannot be quantitatively analyzed, the integrity of the embedded watermark is compromised, making copyright verification and information acquisition still difficult. To solve this problem, we establish a large-scale screen-camera image dataset (SCISet) and propose a noise simulation network (NoS-Net). Specifically, we obtain 36,000 screen-camera images under various shooting environments with multiple types of screens and cameras. Then, we use SCISet to train the proposed NoS-Net based on the U-Net architecture, which can learn multi-level and complementary feature information of screen-camera images, enhancing its ability to simulate complex noise. Experimental results show that integrating the proposed NoS-Net into mainstream screen-camera resilient watermarking methods significantly improves their ability to resist screen-camera noise attacks. Furthermore, the diversity of SCISet plays an important role in advancing robust watermarking research.