# SOLIDO: A Robust Watermarking Method for Speech Synthesis via Low-Rank Adaptation

arXiv: https://arxiv.org/abs/2504.15035 · HTML: https://mlsanity.com/paper/2504.15035/

## Key facts

- Cited by 1 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 26 of its 40 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/)

## Builds on (most cited first, 26 of 26)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [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)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2108.06098/) (2021)
- [PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior](https://mlsanity.com/paper/2106.06406/) (2021)
- [Proactive Detection of Voice Cloning with Localized Watermarking](https://mlsanity.com/paper/2401.17264/) (2024)
- [A survey of deep neural network watermarking techniques](https://mlsanity.com/paper/2103.09274/) (2021)
- [SoK: Watermarking for AI-Generated Content](https://mlsanity.com/paper/2411.18479/) (2024)
- [WavMark: Watermarking for Audio Generation](https://mlsanity.com/paper/2308.12770/) (2023)
- [AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA](https://mlsanity.com/paper/2405.11135/) (2024)
- [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)
- [GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis](https://mlsanity.com/paper/2407.10471/) (2024)
- [Latent Watermarking of Audio Generative Models](https://mlsanity.com/paper/2409.02915/) (2024)
- [SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI](https://mlsanity.com/paper/2411.11478/) (2024)
- [Collaborative Watermarking for Adversarial Speech Synthesis](https://mlsanity.com/paper/2309.15224/) (2023)
- [TraceableSpeech: Towards Proactively Traceable Text-to-Speech with Watermarking](https://mlsanity.com/paper/2406.04840/) (2024)
- [Attributable-Watermarking of Speech Generative Models](https://mlsanity.com/paper/2202.08900/) (2022)

## Built on by (most cited first, 1 of 1)

- [GAMW: Generation-Aware Mel-Domain Watermarking for TTS Traceability](https://mlsanity.com/paper/2610.15082/) (2026)

## Abstract (arXiv)

The accelerated advancement of speech generative models has given rise to security issues, including model infringement and unauthorized abuse of content. Although existing generative watermarking techniques have proposed corresponding solutions, most methods require substantial computational overhead and training costs. In addition, some methods have limitations in robustness when handling variable-length inputs. To tackle these challenges, we propose textscSOLIDO, a novel generative watermarking method that integrates parameter-efficient fine-tuning with speech watermarking through low-rank adaptation (LoRA) for speech diffusion models. Concretely, the watermark encoder converts the watermark to align with the input of diffusion models. To achieve precise watermark extraction from variable-length inputs, the watermark decoder based on depthwise separable convolution is designed for watermark recovery. To further enhance speech generation performance and watermark extraction capability, we propose a speech-driven lightweight fine-tuning strategy, which reduces computational overhead through LoRA. Comprehensive experiments demonstrate that the proposed method ensures high-fidelity watermarked speech even at a large capacity of 2000 bps. Furthermore, against common individual and compound speech attacks, our SOLIDO achieves a maximum average extraction accuracy of 99.20% and 98.43%, respectively. It surpasses other state-of-the-art methods by nearly 23% in resisting time-stretching attacks.