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arXiv · 2604.19090

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
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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 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.

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JinFeng Xie, Chengfu Ou, Peipeng Yu, Xiaoyu Zhou, Dingding Huang, Jianwei Fei, Zixuan Shen, Zhihua Xia. 2026-04-21. Dual-Guard: Dual-Channel Latent Watermarking for Provenance and Tamper Localization in Diffusion Images. https://arxiv.org/abs/2604.19090

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