# DiffuseTrace: A Transparent and Flexible Watermarking Scheme for Latent Diffusion Model

arXiv: https://arxiv.org/abs/2405.02696 · HTML: https://mlsanity.com/paper/2405.02696/

## Key facts

- Cited by 21 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 40 of its 50 references that are in the map.
- First posted on arXiv 4 May 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 40)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [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)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023)

## Built on by (most cited first, 21 of 21)

- [Watermark Anything with Localized Messages](https://mlsanity.com/paper/2411.07231/) (2024)
- [WMAdapter: Adding WaterMark Control to Latent Diffusion Models](https://mlsanity.com/paper/2406.08337/) (2024)
- [SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models](https://mlsanity.com/paper/2412.04852/) (2024)
- [Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking](https://mlsanity.com/paper/2505.12667/) (2025)
- [Steganalysis on Digital Watermarking: Is Your Defense Truly Impervious?](https://mlsanity.com/paper/2406.09026/) (2024)
- [SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution](https://mlsanity.com/paper/2412.10049/) (2024)
- [GaussianSeal: Rooting Adaptive Watermarks for 3D Gaussian Generation Model](https://mlsanity.com/paper/2503.00531/) (2025)
- [Safe-VAR: Safe Visual Autoregressive Model for Text-to-Image Generative Watermarking](https://mlsanity.com/paper/2503.11324/) (2025)
- [Learning to Watermark in the Latent Space of Generative Models](https://mlsanity.com/paper/2601.16140/) (2026)
- [Cert-LAS: Toward Certified Model Ownership Verification for Text-to-Image Diffusion Models via Layer-Adaptive Smoothing](https://mlsanity.com/paper/2605.29809/) (2026)
- [SAT-LDM: Provably Generalizable Image Watermarking for Latent Diffusion Models with Self-Augmented Training](https://mlsanity.com/paper/2501.00463/) (2024)
- [PT-Mark: Invisible Watermarking for Text-to-image Diffusion Models via Semantic-aware Pivotal Tuning](https://mlsanity.com/paper/2504.10853/) (2025)
- [PCDiff: Proactive Control for Ownership Protection in Diffusion Models with Watermark Compatibility](https://mlsanity.com/paper/2504.11774/) (2025)
- [Embedding Watermarks in Diffusion Process for Model Intellectual Property Protection](https://mlsanity.com/paper/2410.22445/) (2024)
- [SWA-LDM: Toward Stealthy Watermarks for Latent Diffusion Models](https://mlsanity.com/paper/2502.10495/) (2025)
- [WaterFlow: Learning Fast & Robust Watermarks using Stable Diffusion](https://mlsanity.com/paper/2504.12354/) (2025)
- [VTONGuard: Automatic Detection and Authentication of AI-Generated Virtual Try-On Content](https://mlsanity.com/paper/2601.13951/) (2026)
- [Proof-of-Authorship for Diffusion-based AI Generated Content](https://mlsanity.com/paper/2603.17513/) (2026)
- [FedOT: Ownership Verification and Leakage Tracing via Watermarks for Federated LDMs](https://mlsanity.com/paper/2606.22875/) (2026)
- [Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)](https://mlsanity.com/paper/2607.18263/) (2026)
- [Diffusion-Generated Image Watermarking: A Two-Axis Taxonomy and Three Protocol-Bounded Case Studies](https://mlsanity.com/paper/2610.09755/) (2026)

## Abstract (arXiv)

Latent Diffusion Models (LDMs) enable a wide range of applications but raise ethical concerns regarding illegal utilization. Adding watermarks to generative model outputs is a vital technique employed for copyright tracking and mitigating potential risks associated with Artificial Intelligence (AI)-generated contents. However, post-processed watermarking methods are unable to withstand generative watermark attacks and there exists a trade-off between image fidelity and watermark strength. Therefore, we propose a novel technique called DiffuseTrace. DiffuseTrace does not rely on fine-tuning of the diffusion model components. The multi-bit watermark is a embedded into the image space semantically without compromising image quality. The watermark component can be utilized as a plug-in in arbitrary diffusion models. We validate through experiments the effectiveness and flexibility of DiffuseTrace. Under 8 types of image processing watermark attacks and 3 types of generative watermark attacks, DiffuseTrace maintains watermark detection rate of 99% and attribution accuracy of over 94%.