# Watermarking Diffusion Model

arXiv: https://arxiv.org/abs/2305.12502 · HTML: https://mlsanity.com/paper/2305.12502/

## Key facts

- Cited by 37 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 25 of its 29 references that are in the map.
- First posted on arXiv 21 May 2023.

## 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, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [Vector-quantized Image Modeling with Improved VQGAN](https://mlsanity.com/paper/2110.04627/) (2021)
- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [Raising the Cost of Malicious AI-Powered Image Editing](https://mlsanity.com/paper/2302.06588/) (2023)
- [Entangled Watermarks as a Defense against Model Extraction](https://mlsanity.com/paper/2002.12200/) (2020)
- [Adversarial Frontier Stitching for Remote Neural Network Watermarking](https://mlsanity.com/paper/1711.01894/) (2017)
- [Prompt Stealing Attacks Against Text-to-Image Generation Models](https://mlsanity.com/paper/2302.09923/) (2023)
- [SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders](https://mlsanity.com/paper/2201.11692/) (2022)
- [Backdoor Attacks Against Dataset Distillation](https://mlsanity.com/paper/2301.01197/) (2023)

## Built on by (most cited first, 30 of 37)

- [Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances](https://mlsanity.com/paper/2410.18775/) (2024)
- [RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identification](https://mlsanity.com/paper/2404.14055/) (2024)
- [Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety](https://mlsanity.com/paper/2502.05206/) (2025)
- [Copyright Protection in Generative AI: A Technical Perspective](https://mlsanity.com/paper/2402.02333/) (2024)
- [SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models](https://mlsanity.com/paper/2412.04852/) (2024)
- [Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models](https://mlsanity.com/paper/2404.09401/) (2024)
- [SeqMIA: Sequential-Metric Based Membership Inference Attack](https://mlsanity.com/paper/2407.15098/) (2024)
- [Hidden in the Noise: Two-Stage Robust Watermarking for Images](https://mlsanity.com/paper/2412.04653/) (2024)
- [Intellectual Property Protection of Diffusion Models via the Watermark Diffusion Process](https://mlsanity.com/paper/2306.03436/) (2023)
- [ProMark: Proactive Diffusion Watermarking for Causal Attribution](https://mlsanity.com/paper/2403.09914/) (2024)
- [Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models](https://mlsanity.com/paper/2504.15026/) (2025)
- [Towards Understanding Unsafe Video Generation](https://mlsanity.com/paper/2407.12581/) (2024)
- [JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model Edits](https://mlsanity.com/paper/2406.03720/) (2024)
- [Catch You Everything Everywhere: Guarding Textual Inversion via Concept Watermarking](https://mlsanity.com/paper/2309.05940/) (2023)
- [Warfare:Breaking the Watermark Protection of AI-Generated Content](https://mlsanity.com/paper/2310.07726/) (2023)
- [Invisible Watermarking for Audio Generation Diffusion Models](https://mlsanity.com/paper/2309.13166/) (2023)
- [NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models](https://mlsanity.com/paper/2510.13793/) (2025)
- [A Somewhat Robust Image Watermark against Diffusion-based Editing Models](https://mlsanity.com/paper/2311.13713/) (2023)
- [GaussianSeal: Rooting Adaptive Watermarks for 3D Gaussian Generation Model](https://mlsanity.com/paper/2503.00531/) (2025)
- [Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?](https://mlsanity.com/paper/2503.09122/) (2025)
- [Visual Watermarking in the Era of Diffusion Models: Advances and Challenges](https://mlsanity.com/paper/2505.08197/) (2025)
- [Hey That's Mine Imperceptible Watermarks are Preserved in Diffusion Generated Outputs](https://mlsanity.com/paper/2308.11123/) (2023)
- [SolidMark: Evaluating Image Memorization in Generative Models](https://mlsanity.com/paper/2503.00592/) (2025)
- [TimeWak: Temporal Chained-Hashing Watermark for Time Series Data](https://mlsanity.com/paper/2506.06407/) (2025)
- [When There Is No Decoder: Removing Watermarks from Stable Diffusion Models in a No-box Setting](https://mlsanity.com/paper/2507.03646/) (2025)
- [Responsible Diffusion: A Comprehensive Survey on Safety, Ethics, and Trust in Diffusion Models](https://mlsanity.com/paper/2509.22723/) (2025)
- [Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing](https://mlsanity.com/paper/2605.29809/) (2026)
- [Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable](https://mlsanity.com/paper/2405.00466/) (2024)
- [Robustness of Watermarking on Text-to-Image Diffusion Models](https://mlsanity.com/paper/2408.02035/) (2024)
- [Embedding Watermarks in Diffusion Process for Model Intellectual Property Protection](https://mlsanity.com/paper/2410.22445/) (2024)

## Abstract (arXiv)

The availability and accessibility of diffusion models (DMs) have significantly increased in recent years, making them a popular tool for analyzing and predicting the spread of information, behaviors, or phenomena through a population. Particularly, text-to-image diffusion models (e.g., DALLE 2 and Latent Diffusion Models (LDMs) have gained significant attention in recent years for their ability to generate high-quality images and perform various image synthesis tasks. Despite their widespread adoption in many fields, DMs are often susceptible to various intellectual property violations. These can include not only copyright infringement but also more subtle forms of misappropriation, such as unauthorized use or modification of the model. Therefore, DM owners must be aware of these potential risks and take appropriate steps to protect their models. In this work, we are the first to protect the intellectual property of DMs. We propose a simple but effective watermarking scheme that injects the watermark into the DMs and can be verified by the pre-defined prompts. In particular, we propose two different watermarking methods, namely NAIVEWM and FIXEDWM. The NAIVEWM method injects the watermark into the LDMs and activates it using a prompt containing the watermark. On the other hand, the FIXEDWM is considered more advanced and stealthy compared to the NAIVEWM, as it can only activate the watermark when using a prompt containing a trigger in a fixed position. We conducted a rigorous evaluation of both approaches, demonstrating their effectiveness in watermark injection and verification with minimal impact on the LDM's functionality.