# Latent Watermarking of Audio Generative Models

arXiv: https://arxiv.org/abs/2409.02915 · HTML: https://mlsanity.com/paper/2409.02915/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2024.

## Built on by (most cited first, 16 of 16)

- [SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI](https://mlsanity.com/paper/2411.11478/) (2024)
- [A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?](https://mlsanity.com/paper/2505.19663/) (2025)
- [Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation](https://mlsanity.com/paper/2507.17937/) (2025)
- [HarmonicAttack: An Adaptive Cross-Domain Audio Watermark Removal](https://mlsanity.com/paper/2511.21577/) (2025)
- [SOLIDO: A Robust Watermarking Method for Speech Synthesis via Low-Rank Adaptation](https://mlsanity.com/paper/2504.15035/) (2025)
- [VocBulwark: Towards Practical Generative Speech Watermarking via Additional-Parameter Injection](https://mlsanity.com/paper/2601.22556/) (2026)
- [Latent-Mark: An Audio Watermark Robust to Neural Codec Compression](https://mlsanity.com/paper/2603.05310/) (2026)
- [LOCKEY: A Novel Approach to Model Authentication and Deepfake Tracking](https://mlsanity.com/paper/2409.07743/) (2024)
- [Targeted Data Poisoning for Black-Box Audio Datasets Ownership Verification](https://mlsanity.com/paper/2503.10269/) (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)
- [Multi-bit Audio Watermarking](https://mlsanity.com/paper/2510.01968/) (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)
- [Tokens Change, Structure Endures: Spectral Watermarking for Generated Speech](https://mlsanity.com/paper/2609.33774/) (2026)

## Abstract (arXiv)

The advancements in audio generative models have opened up new challenges in their responsible disclosure and the detection of their misuse. In response, we introduce a method to watermark latent generative models by a specific watermarking of their training data. The resulting watermarked models produce latent representations whose decoded outputs are detected with high confidence, regardless of the decoding method used. This approach enables the detection of the generated content without the need for a post-hoc watermarking step. It provides a more secure solution for open-sourced models and facilitates the identification of derivative works that fine-tune or use these models without adhering to their license terms. Our results indicate for instance that generated outputs are detected with an accuracy of more than 75% at a false positive rate of 10-3, even after fine-tuning the latent generative model.