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Dual-Guard: Dual-Channel Latent Watermarking for Provenance and Tamper Localization in Diffusion Images

arXiv Security Archived Apr 22, 2026 ✓ Full text saved

arXiv:2604.19090v1 Announce Type: new Abstract: The rapid adoption of diffusion-based generative models has intensified concerns over the attribution and integrity of AI-generated content (AIGC). Existing single-domain watermarking methods either fail under regeneration, remain vulnerable to black-box reprompting that enables adversarial framing, or provide no spatial evidence for tampered regions. We propose Dual-Guard, a dual-channel latent watermarking framework for practical provenance verif

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    Computer Science > Cryptography and Security [Submitted on 21 Apr 2026] Dual-Guard: Dual-Channel Latent Watermarking for Provenance and Tamper Localization in Diffusion Images JinFeng Xie, Chengfu Ou, Peipeng Yu, Xiaoyu Zhou, Dingding Huang, Jianwei Fei, Zixuan Shen, Zhihua Xia The rapid adoption of diffusion-based generative models has intensified concerns over the attribution and integrity of AI-generated content (AIGC). Existing single-domain watermarking methods either fail under regeneration, remain vulnerable to black-box reprompting that enables adversarial framing, or provide no spatial evidence for tampered regions. We propose Dual-Guard, a dual-channel latent watermarking framework for practical provenance verification, framing resistance, and region-level tamper localization. Dual-Guard combines two complementary anchors: a Gaussian Shading watermark in the initial diffusion noise as a global provenance signal, and a Latent Fingerprint Codec in the final denoised latent as a structured content anchor. Reprompting tends to preserve the former while breaking the latter, whereas localized edits disturb the content anchor only in tampered regions. In Full mode on a 2,400-sample benchmark, Dual-Guard keeps clean-image authentication false rejection and tamper false alarm below one half of one percent, while maintaining near-complete detection under reprompting, diffusion editing, and eight local tampering attacks. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.19090 [cs.CR]   (or arXiv:2604.19090v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.19090 Focus to learn more Submission history From: JinFeng Xie [view email] [v1] Tue, 21 Apr 2026 05:03:42 UTC (3,615 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
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    ◬ AI & Machine Learning
    Published
    Apr 22, 2026
    Archived
    Apr 22, 2026
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