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GenPTW: Latent Image Watermarking for Provenance Tracing and Tamper Localization

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Gan, Zhenliang, Liu, Chunya, Tang, Yichao, Wang, Binghao, Cui, Shiwen, Wang, Weiqiang, Zhang, Xinpeng
Format:	Preprint
Publié:	2025
Sujets:	Cryptography and Security
Accès en ligne:	Acceder al recurso
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author	Gan, Zhenliang Liu, Chunya Tang, Yichao Wang, Binghao Cui, Shiwen Wang, Weiqiang Zhang, Xinpeng
author_facet	Gan, Zhenliang Liu, Chunya Tang, Yichao Wang, Binghao Cui, Shiwen Wang, Weiqiang Zhang, Xinpeng
contents	The proliferation of generative image models has revolutionized AIGC creation while amplifying concerns over content provenance and manipulation forensics. Existing methods are typically either unable to localize tampering or restricted to specific generative settings, limiting their practical utility. We propose \textbf{GenPTW}, a \textbf{Gen}eral watermarking framework that unifies \textbf{P}rovenance tracing and \textbf{T}amper localization in latent space. It supports both in-generation and post-generation embedding without altering the generative process, and is plug-and-play compatible with latent diffusion models (LDMs) and visual autoregressive (VAR) models. To achieve precise provenance tracing and tamper localization, we embed the watermark using two complementary mechanisms: cross-attention fusion aligned with latent semantics and spatial fusion providing explicit spatial guidance for edit sensitivity. A tamper-aware extractor jointly conducts provenance tracing and tamper localization by leveraging watermark features together with high-frequency features. Experiments show that GenPTW maintains high visual fidelity and strong robustness against diverse AIGC-editing.
format	Preprint
id	arxiv_https___arxiv_org_abs_2504_19567
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	GenPTW: Latent Image Watermarking for Provenance Tracing and Tamper Localization Gan, Zhenliang Liu, Chunya Tang, Yichao Wang, Binghao Cui, Shiwen Wang, Weiqiang Zhang, Xinpeng Cryptography and Security The proliferation of generative image models has revolutionized AIGC creation while amplifying concerns over content provenance and manipulation forensics. Existing methods are typically either unable to localize tampering or restricted to specific generative settings, limiting their practical utility. We propose \textbf{GenPTW}, a \textbf{Gen}eral watermarking framework that unifies \textbf{P}rovenance tracing and \textbf{T}amper localization in latent space. It supports both in-generation and post-generation embedding without altering the generative process, and is plug-and-play compatible with latent diffusion models (LDMs) and visual autoregressive (VAR) models. To achieve precise provenance tracing and tamper localization, we embed the watermark using two complementary mechanisms: cross-attention fusion aligned with latent semantics and spatial fusion providing explicit spatial guidance for edit sensitivity. A tamper-aware extractor jointly conducts provenance tracing and tamper localization by leveraging watermark features together with high-frequency features. Experiments show that GenPTW maintains high visual fidelity and strong robustness against diverse AIGC-editing.
title	GenPTW: Latent Image Watermarking for Provenance Tracing and Tamper Localization
topic	Cryptography and Security
url	https://arxiv.org/abs/2504.19567