NiMark: A Non-intrusive Watermarking Framework against Screen-shooting Attacks

2026-01-17

AI Summary

This work proposes an end-to-end, non-intrusive watermarking framework to address data leakage caused by screen-recapture attacks. The method achieves robust protection against physical capture without modifying the original image or introducing any perceptual distortion. Its core innovations include a Sigmoid-Gated XOR (SG-XOR) logical binding mechanism designed to eliminate structural shortcuts, complemented by a two-stage training strategy and an image restoration module that effectively mitigates the domain shift induced by screen recapture. Experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art methods under both digital tampering and real-world screen-recapture noise, offering lossless embedding and strong resilience to noise.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Privacy
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Unauthorized screen-shooting poses a critical data leakage risk. Resisting screen-shooting attacks typically requires high-strength watermark embedding, inevitably degrading the cover image. To resolve the robustness-fidelity conflict, non-intrusive watermarking has emerged as a solution by constructing logical verification keys without altering the original content. However, existing non-intrusive schemes lack the capacity to withstand screen-shooting noise. While deep learning offers a potential remedy, we observe that directly applying it leads to a previously underexplored failure mode, the Structural Shortcut: networks tend to learn trivial identity mappings and neglect the image-watermark binding. Furthermore, even when logical binding is enforced, standard training strategies cannot fully bridge the noise gap, yielding suboptimal robustness against physical distortions. In this paper, we propose NiMark, an end-to-end framework addressing these challenges. First, to eliminate the structural shortcut, we introduce the Sigmoid-Gated XOR (SG-XOR) estimator to enable gradient propagation for the logical operation, effectively enforcing rigid image-watermark binding. Second, to overcome the robustness bottleneck, we devise a two-stage training strategy integrating a restorer to bridge the domain gap caused by screen-shooting noise. Experiments demonstrate that NiMark consistently outperforms representative state-of-the-art methods against both digital attacks and screen-shooting noise, while maintaining zero visual distortion.

Problem

• screen-shooting attacks
• non-intrusive watermarking
• structural shortcut
• image-watermark binding
• robustness-fidelity conflict

Innovation

• non-intrusive watermarking
• screen-shooting attack
• structural shortcut
• SG-XOR estimator
• two-stage training