arXiv subjects

Mingyue Chen

Publications and source records attributed to Mingyue Chen.

3 recordsLinked to original sources

arXiv2026

Screen-Conditioned Watermarking Against Multi-Screen Collusion Attacks

Screen-shooting poses a significant threat to confidential information protection. While existing screen-shooting watermarking methods enable copyright verification, the copyrighted images carrying the same copyright watermark across different screens often exhibit highly similar and estimable watermark patterns. These shared patterns can be exploited for watermark removal and forgery, a threat we term the multi-screen collusion attack. To mitigate this threat, we propose CoMSMark, a collusion-resistant image-agnostic watermarking framework for multi-screen shooting, which reduces shared residual components across screens to resist multi-screen collusion attacks. Specifically, we incorporate screen ID through a style modulation mechanism, enabling the encoder to generate screen-specific watermark residuals for reliable source attribution. We further introduce a collusion suppression loss that reduces shared residual components and encourages high-entropy predictions for forged samples, improving resistance to collusion attacks. Finally, to enable efficient large-scale distribution, CoMSMark employs an image-agnostic encoding paradigm that generates watermark residuals independently of image content. Extensive experiments demonstrate that CoMSMark effectively resists both collusion-based watermark removal and forgery. It maintains an average watermark accuracy above 90% under removal attacks while keeping forged-watermark accuracy near 50%. Moreover, CoMSMark achieves competitive robustness under diverse screen-shooting conditions, including varying capture distances and angles.

cs.CR↗

arXiv2026

New Constraints on Lorentz Invariance Violation at High Redshifts from Multiband of GRBs

In the gravity quantum theory, the quantization of spacetime may lead to the modification of the dispersion relation between the energy and the momentum and the Lorentz invariance violation (LIV). High energy and long-distance gamma-ray bursts (GRBs) observations in the universe provide a unique opportunity to test the possibility of LIV. In this work, we use 88 time delays from GRBs ($0.117 < z < 6.29$), and provide a cosmological model-independent approach based on the luminosity distance data from 174 GRBs to test LIV. Combining the observation data from multiband of GRBs provides us with an opportunity to mitigate the potential systematic errors arising from variations in the physical characteristics among diverse object populations, and to add a higher redshift dataset for testing the energy-dependent velocity caused by the corrected dispersion relationship of photons. These robust limits of the energy scale for the linear and quadratic LIV effects are $E_{\mathrm{QG},1} \ge 1.5\times 10^{15}$ GeV, and $E_{\mathrm{QG},2} \ge 8.5\times 10^{9}$ GeV, respectively. It exhibits a significantly reduced value compared to the energy scale of Planck in both scenarios of linear and quadratic LIV.

astro-ph.HE↗

arXiv2025

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

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 modeled 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 state-of-the-art methods.

cs.CV↗