Sim-to-Real: An Unsupervised Noise Layer for Screen-Camera Watermarking Robustness
Journal:
arXiv
Published Date:
Apr 26, 2025
Abstract
Unauthorized screen capturing and dissemination pose severe security threats
such as data leakage and information theft. Several studies propose robust
watermarking methods to track the copyright of Screen-Camera (SC) images,
facilitating post-hoc certification against infringement. These techniques
typically employ heuristic mathematical modeling or supervised neural network
fitting as the noise layer, to enhance watermarking robustness against SC.
However, both strategies cannot fundamentally achieve an effective
approximation of SC noise. Mathematical simulation suffers from biased
approximations due to the incomplete decomposition of the noise and the absence
of interdependence among the noise components. Supervised networks require
paired data to train the noise-fitting model, and it is difficult for the model
to learn all the features of the noise. To address the above issues, we propose
Simulation-to-Real (S2R). Specifically, an unsupervised noise layer employs
unpaired data to learn the discrepancy between the modeling simulated noise
distribution and the real-world SC noise distribution, rather than directly
learning the mapping from sharp images to real-world images. Learning this
transformation from simulation to reality is inherently simpler, as it
primarily involves bridging the gap in noise distributions, instead of the
complex task of reconstructing fine-grained image details. Extensive
experimental results validate the efficacy of the proposed method,
demonstrating superior watermark robustness and generalization compared to
those of state-of-the-art methods.
