# Diffusion-Generated Image Watermarking: A Two-Axis Taxonomy and Three Protocol-Bounded Case Studies

arXiv: https://arxiv.org/abs/2610.09755 · HTML: https://mlsanity.com/paper/2610.09755/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 4 of 4)

- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [DiffuseTrace: A Transparent and Flexible Watermarking Scheme for Latent Diffusion Model](https://mlsanity.com/paper/2405.02696/) (2024)
- [VideoMark: A Distortion-Free Robust Watermarking Framework for Video Diffusion Models](https://mlsanity.com/paper/2504.16359/) (2025)
- [On the Information-Theoretic Fragility of Robust Watermarking under Diffusion Editing](https://mlsanity.com/paper/2511.10933/) (2025)

## Abstract (arXiv)

Watermarking diffusion-generated images requires balancing provenance signals with image quality, robustness, and computational cost. This work organizes methods along two axes: insertion mechanism and primary signal-bearing representation, and formalizes a representative zT-Fourier pipeline for verification and identification. We then use the taxonomy to structure three protocol-bounded case studies. The first examines associations among frequency integrity, detection, quality, and cropping behavior. The second revisits persistence under seed-linked and seed-independent editing and formulates a scoped Semantic Imprinting Hypothesis without claiming a localized carrier or causal mechanism. The third studies single-shot VAE-latent phase modulation, including its efficiency, regeneration robustness, and robustness--quality operating points. Finally, we separate four content-level attack families from model/pipeline adaptation, propose corresponding evaluation protocols and testable conjectures for parameter-tuning threats, and identify additional temporal extensions for video. These analyses do not establish a universal ranking; instead, they provide a framework for matched, protocol-aware comparisons of watermarking systems for diffusion-generated images.