A Practical Distortion Simulator for Screen-Shooting Resilient Image Watermarking in Consumer Electronic Applications

Zhang, Yulin; Ni, Jiangqun; Malwi, Wajdan Al; Asiri, Fatima; Ahmad, Jawad; Jiang, Donghua

Abstract

Nowadays screen-shooting resilient watermarking still remains as a predictive and challenging area of research for proactive data protection in consumer electronic applications. Present deep learning-based methodologies embrace end-to-end frameworks and devise specialized noise layer to simulate the distortions introduced in cross-media transmission between consumer electronics. Typically, the noise layer is designed as a deterministic image-to-image network to simulate the transformation from the clean images to the screen-captured ones, overlooking the randomness of real-world distortions, which makes it challenging to satisfy the demands of various consumer electronics and application scenarios. To address this issue, we conceptualize the screen-shooting channel as an image degradation model and accordingly develop the Resolver-Simulator framework (ReSim). The involved screen-shooting simulator is designed as a conditional image-to-image network to learn the exact degradation function. By taking advantage of the pre-trained parameter resolver, the noise components can be disentangled to compose the instance sets. Then the set sampling strategy is adopted to obtain the noise instances for realistic screen-shooting simulation for unseen images. Experimental results demonstrate that the proposed scheme outperforms the previous arts in terms of real-world robustness as well as exhibits high computational efficiency to enable real-time security solutions in consumer devices.

Citation

Zhang, Y., Ni, J., Malwi, W. A., Asiri, F., Ahmad, J., & Jiang, D. (2025). A Practical Distortion Simulator for Screen-Shooting Resilient Image Watermarking in Consumer Electronic Applications. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/tce.2025.3547721

• Journal Article Type: Article
• Online Publication Date: Mar 3, 2025
• Publication Date: 2025
• Deposit Date: Jun 23, 2025
• Journal: IEEE Transactions on Consumer Electronics
• Print ISSN: 0098-3063
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/tce.2025.3547721
• Keywords: Deep learning, robust image watermarking, screen-shooting simulator, consumer electronic applications, predictive security