# Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models

arXiv: https://arxiv.org/abs/2504.15026 · HTML: https://mlsanity.com/paper/2504.15026/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 15 of its 84 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)
- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 15 of 15)

- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust](https://mlsanity.com/paper/2305.20030/) (2023)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [A Recipe for Watermarking Diffusion Models](https://mlsanity.com/paper/2303.10137/) (2023)
- [WAVES: Benchmarking the Robustness of Image Watermarks](https://mlsanity.com/paper/2401.08573/) (2024)
- [Black-Box Forgery Attacks on Semantic Watermarks for Diffusion Models](https://mlsanity.com/paper/2412.03283/) (2024)
- [DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models](https://mlsanity.com/paper/2306.04642/) (2023)
- [Watermarking Diffusion Model](https://mlsanity.com/paper/2305.12502/) (2023)
- [On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark](https://mlsanity.com/paper/2502.04901/) (2025)
- [Ideal Pseudorandom Codes](https://mlsanity.com/paper/2411.05947/) (2024)
- [Towards A Correct Usage of Cryptography in Semantic Watermarks for Diffusion Models](https://mlsanity.com/paper/2503.11404/) (2025)

## Built on by (most cited first, 12 of 12)

- [AEDR: Training-Free AI-Generated Image Attribution via Autoencoder Double-Reconstruction](https://mlsanity.com/paper/2507.18988/) (2025)
- [Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey](https://mlsanity.com/paper/2510.02384/) (2025)
- [SemBind: Binding Diffusion Watermarks to Semantics Against Black-Box Forgery Attacks](https://mlsanity.com/paper/2601.20310/) (2026)
- [Towards Robust Content Watermarking Against Removal and Forgery Attacks](https://mlsanity.com/paper/2604.06662/) (2026)
- [OSI: One-step Inversion Excels in Extracting Diffusion Watermarks](https://mlsanity.com/paper/2602.09494/) (2026)
- [VIDSTAMP: A Temporally-Aware Watermark for Ownership and Integrity in Video Diffusion Models](https://mlsanity.com/paper/2505.01406/) (2025)
- [AuthenLoRA: Entangling Stylization with Imperceptible Watermarks for Copyright-Secure LoRA Adapters](https://mlsanity.com/paper/2511.21216/) (2025)
- [FARI: Robust One-Step Inversion for Watermarking in Diffusion Models](https://mlsanity.com/paper/2607.26723/) (2026)
- [WMVLM: Evaluating Diffusion Model Image Watermarking via Vision-Language Models](https://mlsanity.com/paper/2601.21610/) (2026)
- [mAVE: A Watermark for Joint Audio-Visual Generation Models](https://mlsanity.com/paper/2603.07090/) (2026)
- [DNA: Dual-stage Native Attribution for Generated Image Source Tracing](https://mlsanity.com/paper/2607.13685/) (2026)
- [Membership is Ownership: A Robust Ownership Verification Framework for Diffusion Models](https://mlsanity.com/paper/2608.28929/) (2026)

## Abstract (arXiv)

Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. Existing methods primarily focus on ensuring that watermark embedding does not degrade the model performance. However, they often overlook critical challenges in real-world deployment scenarios, such as the complexity of watermark key management, user-defined generation parameters, and the difficulty of verification by arbitrary third parties. To address this issue, we propose Gaussian Shading++, a diffusion model watermarking method tailored for real-world deployment. We propose a double-channel design that leverages pseudorandom error-correcting codes to encode the random seed required for watermark pseudorandomization, achieving performance-lossless watermarking under a fixed watermark key and overcoming key management challenges. Additionally, we model the distortions introduced during generation and inversion as an additive white Gaussian noise channel and employ a novel soft decision decoding strategy during extraction, ensuring strong robustness even when generation parameters vary. To enable third-party verification, we incorporate public key signatures, which provide a certain level of resistance against forgery attacks even when model inversion capabilities are fully disclosed. Extensive experiments demonstrate that Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness, making it a more practical solution for real-world deployment.