The rapid development of generative image models has brought tremendous opportunities to AI-generated content (AIGC) creation, while also introducing critical challenges in ensuring content authenticity and copyright ownership. Existing image watermarking methods, though partially effective, often rely on post-processing or reference images, and struggle to balance fidelity, robustness, and tamper localization. To address these limitations, we propose GenPTW, an In-Generation image watermarking framework for latent diffusion models (LDMs), which integrates Provenance Tracing and Tamper Localization into a unified Watermark-based design. It embeds structured watermark signals during the image generation phase, enabling unified provenance tracing and tamper localization. For extraction, we construct a frequency-coordinated decoder to improve robustness and localization precision in complex editing scenarios. Additionally, a distortion layer that simulates AIGC editing is introduced to enhance robustness. Extensive experiments demonstrate that GenPTW outperforms existing methods in image fidelity, watermark extraction accuracy, and tamper localization performance, offering an efficient and practical solution for trustworthy AIGC image generation.
View on arXiv@article{gan2025_2504.19567,
title={ GenPTW: In-Generation Image Watermarking for Provenance Tracing and Tamper Localization },
author={ Zhenliang Gan and Chunya Liu and Yichao Tang and Binghao Wang and Weiqiang Wang and Xinpeng Zhang },
journal={arXiv preprint arXiv:2504.19567},
year={ 2025 }
}