# ROBIN: Robust and Invisible Watermarks for Diffusion Models with Adversarial Optimization

arXiv: https://arxiv.org/abs/2411.03862 · HTML: https://mlsanity.com/paper/2411.03862/

## Key facts

- Cited by 46 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 43 of its 59 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## 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, 30 of 43)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [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)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://mlsanity.com/paper/2202.09778/) (2022)
- [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://mlsanity.com/paper/2210.11427/) (2022)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [The Stable Signature: Rooting Watermarks in Latent Diffusion Models](https://mlsanity.com/paper/2303.15435/) (2023)
- [EDICT: Exact Diffusion Inversion via Coupled Transformations](https://mlsanity.com/paper/2211.12446/) (2022)
- [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023)
- [Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data](https://mlsanity.com/paper/2007.08457/) (2020)
- [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)
- [Invisible Image Watermarks Are Provably Removable Using Generative AI](https://mlsanity.com/paper/2306.01953/) (2023)
- [Anti-DreamBooth: Protecting users from personalized text-to-image synthesis](https://mlsanity.com/paper/2303.15433/) (2023)

## Built on by (most cited first, 30 of 46)

- [The Silent Assistant: NoiseQuery as Implicit Guidance for Goal-Driven Image Generation](https://mlsanity.com/paper/2412.05101/) (2024)
- [First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge](https://mlsanity.com/paper/2508.21072/) (2025)
- [SLICE: Semantic Latent Injection via Compartmentalized Embedding for Image Watermarking](https://mlsanity.com/paper/2603.12749/) (2026)
- [WMCopier: Forging Invisible Image Watermarks on Arbitrary Images](https://mlsanity.com/paper/2503.22330/) (2025)
- [MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models](https://mlsanity.com/paper/2509.10569/) (2025)
- [Guidance Watermarking for Diffusion Models](https://mlsanity.com/paper/2509.22126/) (2025)
- [Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey](https://mlsanity.com/paper/2510.02384/) (2025)
- [Watermarking Discrete Diffusion Language Models](https://mlsanity.com/paper/2511.02083/) (2025)
- [Implicit Bias Injection Attacks against Text-to-Image Diffusion Models](https://mlsanity.com/paper/2504.01819/) (2025)
- [Visual Watermarking in the Era of Diffusion Models: Advances and Challenges](https://mlsanity.com/paper/2505.08197/) (2025)
- [Mitigating Watermark Forgery in Generative Models via Randomized Key Selection](https://mlsanity.com/paper/2507.07871/) (2025)
- [SHIFT: Stochastic Hidden-Trajectory Deflection for Removing Diffusion-based Watermark](https://mlsanity.com/paper/2603.29742/) (2026)
- [Attack-Resistant Watermarking for AIGC Image Forensics via Diffusion-based Semantic Deflection](https://mlsanity.com/paper/2601.06639/) (2026)
- [SIGMark: Scalable In-Generation Watermark with Blind Extraction for Video Diffusion](https://mlsanity.com/paper/2603.02882/) (2026)
- [Towards Robust Content Watermarking Against Removal and Forgery Attacks](https://mlsanity.com/paper/2604.06662/) (2026)
- [PT-Mark: Invisible Watermarking for Text-to-image Diffusion Models via Semantic-aware Pivotal Tuning](https://mlsanity.com/paper/2504.10853/) (2025)
- [TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models](https://mlsanity.com/paper/2505.23312/) (2025)
- [OptMark: Robust Multi-bit Diffusion Watermarking via Inference Time Optimization](https://mlsanity.com/paper/2508.21727/) (2025)
- [RAVEN: Erasing Invisible Watermarks via Novel View Synthesis](https://mlsanity.com/paper/2601.08832/) (2026)
- [OSI: One-step Inversion Excels in Extracting Diffusion Watermarks](https://mlsanity.com/paper/2602.09494/) (2026)
- [Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle](https://mlsanity.com/paper/2608.04314/) (2026)
- [MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection](https://mlsanity.com/paper/2505.24267/) (2025)
- [Off-The-Shelf Image-to-Image Models Are All You Need To Defeat Image Protection Schemes](https://mlsanity.com/paper/2602.22197/) (2026)
- [ShapeMark: Robust and Diversity-Preserving Watermarking for Diffusion Models](https://mlsanity.com/paper/2603.09454/) (2026)
- [Removing the Watermark Is Not Enough: Forensic Stealth in Generative-AI Watermark Removal](https://mlsanity.com/paper/2605.09203/) (2026)
- [Compositional Adversarial Training for Robust Visual Watermarking](https://mlsanity.com/paper/2605.16720/) (2026)
- [LoRA-Key: User-Centric LoRA Watermarking for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2605.29569/) (2026)
- [Watermark Forensics for Generative Models: An Information-Theoretic Perspective](https://mlsanity.com/paper/2607.13003/) (2026)
- [FADE: Adversarial Concept Erasure in Flow Models](https://mlsanity.com/paper/2507.12283/) (2025)
- [Learning Generalizable and Efficient Image Watermarking via Hierarchical Two-Stage Optimization](https://mlsanity.com/paper/2508.08667/) (2025)

## Abstract (arXiv)

Watermarking generative content serves as a vital tool for authentication, ownership protection, and mitigation of potential misuse. Existing watermarking methods face the challenge of balancing robustness and concealment. They empirically inject a watermark that is both invisible and robust and passively achieve concealment by limiting the strength of the watermark, thus reducing the robustness. In this paper, we propose to explicitly introduce a watermark hiding process to actively achieve concealment, thus allowing the embedding of stronger watermarks. To be specific, we implant a robust watermark in an intermediate diffusion state and then guide the model to hide the watermark in the final generated image. We employ an adversarial optimization algorithm to produce the optimal hiding prompt guiding signal for each watermark. The prompt embedding is optimized to minimize artifacts in the generated image, while the watermark is optimized to achieve maximum strength. The watermark can be verified by reversing the generation process. Experiments on various diffusion models demonstrate the watermark remains verifiable even under significant image tampering and shows superior invisibility compared to other state-of-the-art robust watermarking methods. Code is available at https://github.com/Hannah1102/ROBIN.