# PiGW: A Plug-in Generative Watermarking Framework

arXiv: https://arxiv.org/abs/2403.12053 · HTML: https://mlsanity.com/paper/2403.12053/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2024.

## Built on by (most cited first, 2 of 2)

- [Steganalysis on Digital Watermarking: Is Your Defense Truly Impervious?](https://mlsanity.com/paper/2406.09026/) (2024)
- [StyleMark: A Robust Watermarking Method for Art Style Images Against Black-Box Arbitrary Style Transfer](https://mlsanity.com/paper/2412.07129/) (2024)

## Abstract (arXiv)

Integrating watermarks into generative images is a critical strategy for protecting intellectual property and enhancing artificial intelligence security. This paper proposes Plug-in Generative Watermarking (PiGW) as a general framework for integrating watermarks into generative images. More specifically, PiGW embeds watermark information into the initial noise using a learnable watermark embedding network and an adaptive frequency spectrum mask. Furthermore, it optimizes training costs by gradually increasing timesteps. Extensive experiments demonstrate that PiGW enables embedding watermarks into the generated image with negligible quality loss while achieving true invisibility and high resistance to noise attacks. Moreover, PiGW can serve as a plugin for various commonly used generative structures and multimodal generative content types. Finally, we demonstrate how PiGW can also be utilized for detecting generated images, contributing to the promotion of secure AI development. The project code will be made available on GitHub.