# GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis

arXiv: https://arxiv.org/abs/2407.10471 · HTML: https://mlsanity.com/paper/2407.10471/

## 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 27 of its 42 references that are in the map.
- First posted on arXiv 15 Jul 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, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (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)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech](https://mlsanity.com/paper/2105.06337/) (2021)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data](https://mlsanity.com/paper/2007.08457/) (2020)
- [ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech](https://mlsanity.com/paper/2207.06389/) (2022)
- [AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines](https://mlsanity.com/paper/2010.11567/) (2020)
- [Responsible Disclosure of Generative Models Using Scalable Fingerprinting](https://mlsanity.com/paper/2012.08726/) (2020)
- [PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior](https://mlsanity.com/paper/2106.06406/) (2021)
- [A survey of deep neural network watermarking techniques](https://mlsanity.com/paper/2103.09274/) (2021)
- [BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis](https://mlsanity.com/paper/2203.13508/) (2022)
- [Deep Model Intellectual Property Protection via Deep Watermarking](https://mlsanity.com/paper/2103.04980/) (2021)
- [Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack](https://mlsanity.com/paper/2102.04362/) (2021)
- [Detecting Voice Cloning Attacks via Timbre Watermarking](https://mlsanity.com/paper/2312.03410/) (2023)
- [DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking](https://mlsanity.com/paper/2212.02339/) (2022)
- [Copyright Protection and Accountability of Generative AI:Attack, Watermarking and Attribution](https://mlsanity.com/paper/2303.09272/) (2023)
- [LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-Speech](https://mlsanity.com/paper/2308.16569/) (2023)
- [GLA-Grad: A Griffin-Lim Extended Waveform Generation Diffusion Model](https://mlsanity.com/paper/2402.15516/) (2024)
- [CycleGANWM: A CycleGAN watermarking method for ownership verification](https://mlsanity.com/paper/2211.13737/) (2022)

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

- [SoK: Watermarking for AI-Generated Content](https://mlsanity.com/paper/2411.18479/) (2024)
- [SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI](https://mlsanity.com/paper/2411.11478/) (2024)
- [Weakly-supervised Audio Temporal Forgery Localization via Progressive Audio-language Co-learning Network](https://mlsanity.com/paper/2505.01880/) (2025)
- [Self Voice Conversion as an Attack against Neural Audio Watermarking](https://mlsanity.com/paper/2601.20432/) (2026)
- [SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation](https://mlsanity.com/paper/2604.05489/) (2026)
- [HarmonicAttack: An Adaptive Cross-Domain Audio Watermark Removal](https://mlsanity.com/paper/2511.21577/) (2025)
- [Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Models](https://mlsanity.com/paper/2603.16874/) (2026)
- [De-AntiFake: Rethinking the Protective Perturbations Against Voice Cloning Attacks](https://mlsanity.com/paper/2507.02606/) (2025)
- [SOLIDO: A Robust Watermarking Method for Speech Synthesis via Low-Rank Adaptation](https://mlsanity.com/paper/2504.15035/) (2025)
- [Traceable TTS: Toward Watermark-Free TTS with Strong Traceability](https://mlsanity.com/paper/2507.03887/) (2025)
- [MelShield: Robust Mel-Domain Audio Watermarking for Provenance Attribution of AI Generated Synthesized Speech](https://mlsanity.com/paper/2605.01515/) (2026)
- [CRAW: Codec Robust Audio Watermarking](https://mlsanity.com/paper/2609.03107/) (2026)
- [Protecting Your Voice: Temporal-aware Robust Watermarking](https://mlsanity.com/paper/2504.14832/) (2025)
- [TriniMark: A Robust Generative Speech Watermarking Method for Trinity-Level Traceability](https://mlsanity.com/paper/2504.20532/) (2025)
- [Efficient Speech Watermarking for Speech Synthesis via Progressive Knowledge Distillation](https://mlsanity.com/paper/2509.19812/) (2025)
- [UniMark: Artificial Intelligence Generated Content Identification Toolkit](https://mlsanity.com/paper/2512.12324/) (2025)
- [Smark: A Watermark for Text-to-Speech Diffusion Models via Discrete Wavelet Transform](https://mlsanity.com/paper/2512.18791/) (2025)
- [LambdaMark: Semantic Audio Watermarking for Robustness and Radioactivity](https://mlsanity.com/paper/2606.21365/) (2026)
- [MusicMark: A Robust Generative Watermarking Framework for Music Generation](https://mlsanity.com/paper/2607.11117/) (2026)
- [AudioNoisePrints: Model-free audio watermarking using spatial correlation in flow matching TTS](https://mlsanity.com/paper/2608.22186/) (2026)
- [GAMW: Generation-Aware Mel-Domain Watermarking for TTS Traceability](https://mlsanity.com/paper/2610.15082/) (2026)

## Abstract (arXiv)

Amid the burgeoning development of generative models like diffusion models, the task of differentiating synthesized audio from its natural counterpart grows more daunting. Deepfake detection offers a viable solution to combat this challenge. Yet, this defensive measure unintentionally fuels the continued refinement of generative models. Watermarking emerges as a proactive and sustainable tactic, preemptively regulating the creation and dissemination of synthesized content. Thus, this paper, as a pioneer, proposes the generative robust audio watermarking method (Groot), presenting a paradigm for proactively supervising the synthesized audio and its source diffusion models. In this paradigm, the processes of watermark generation and audio synthesis occur simultaneously, facilitated by parameter-fixed diffusion models equipped with a dedicated encoder. The watermark embedded within the audio can subsequently be retrieved by a lightweight decoder. The experimental results highlight Groot's outstanding performance, particularly in terms of robustness, surpassing that of the leading state-of-the-art methods. Beyond its impressive resilience against individual post-processing attacks, Groot exhibits exceptional robustness when facing compound attacks, maintaining an average watermark extraction accuracy of around 95%.