# LambdaMark: Semantic Audio Watermarking for Robustness and Radioactivity

arXiv: https://arxiv.org/abs/2606.21365 · HTML: https://mlsanity.com/paper/2606.21365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 56 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 27 of 27)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [Qwen2-Audio Technical Report](https://mlsanity.com/paper/2407.10759/) (2024)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets](https://mlsanity.com/paper/2104.09494/) (2021)
- [AI Deception: A Survey of Examples, Risks, and Potential Solutions](https://mlsanity.com/paper/2308.14752/) (2023)
- [Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust](https://mlsanity.com/paper/2305.20030/) (2023)
- [Qwen3-TTS Technical Report](https://mlsanity.com/paper/2601.15621/) (2026)
- [RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identification](https://mlsanity.com/paper/2404.14055/) (2024)
- [ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric](https://mlsanity.com/paper/2004.09584/) (2020)
- [Proactive Detection of Voice Cloning with Localized Watermarking](https://mlsanity.com/paper/2401.17264/) (2024)
- [Discovering Interpretable Directions in the Semantic Latent Space of Diffusion Models](https://mlsanity.com/paper/2303.11073/) (2023)
- [WavMark: Watermarking for Audio Generation](https://mlsanity.com/paper/2308.12770/) (2023)
- [Detecting Voice Cloning Attacks via Timbre Watermarking](https://mlsanity.com/paper/2312.03410/) (2023)
- [Scaling up masked audio encoder learning for general audio classification](https://mlsanity.com/paper/2406.06992/) (2024)
- [MiDashengLM: Efficient Audio Understanding with General Audio Captions](https://mlsanity.com/paper/2508.03983/) (2025)
- [AudioMarkBench: Benchmarking Robustness of Audio Watermarking](https://mlsanity.com/paper/2406.06979/) (2024)
- [SilentCipher: Deep Audio Watermarking](https://mlsanity.com/paper/2406.03822/) (2024)
- [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)
- [A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?](https://mlsanity.com/paper/2505.19663/) (2025)
- [Deep Audio Watermarks are Shallow: Limitations of Post-Hoc Watermarking Techniques for Speech](https://mlsanity.com/paper/2504.10782/) (2025)
- [HarmonicAttack: An Adaptive Cross-Domain Audio Watermark Removal](https://mlsanity.com/paper/2511.21577/) (2025)
- [SemanticVocoder: Bridging Audio Generation and Audio Understanding via Semantic Latents](https://mlsanity.com/paper/2602.23333/) (2026)
- [HMARK: Radioactive Multi-Bit Semantic-Latent Watermarking for Diffusion Models](https://mlsanity.com/paper/2512.00094/) (2025)

## Abstract (arXiv)

Recent advances in generative audio have made voice cloning increasingly effortless, enabling voice fraud, impersonation, and other forms of unauthorized use. A common attack finetunes a speech generation model on recordings of a target speaker, allowing the model to synthesize speech in that speaker's voice. Audio watermarking offers a promising defense by embedding detectable signals into audio. A practical watermark must satisfy two key properties: robustness and radioactivity. Existing audio watermarking methods typically embed signals into low-level representations, such as waveforms or spectrograms, which makes them vulnerable to signal-level manipulations and limits their transfer to downstream models. We introduce LambdaMark -- the first generic radioactive watermarking scheme. Unlike all previous approaches, LambdaMark achieves generic radioactivity by embedding multi-bit watermark information into semantic audio latent representations. Our watermarks have semantic interpretation and are thus more likely to be learned by a downstream model through finetuning. LambdaMark includes a lightweight watermark encoder to inject multi-bit message-dependent perturbations into semantic audio representations and a decoder to detect watermark presence and recover the embedded bit information. Encoder and decoder are trained using a custom multi-component loss that preserves fidelity of the watermarked audio, increases bit-level recovery rate, and improves robustness against common distortions and adversarial removal attempts. Experiments show that LambdaMark achieves near-perfect robustness under common distortions. LambdaMark is also the only watermark that is robust against all evaluated removal attacks. Furthermore, LambdaMark exhibits general and robust radioactivity and remains robust to distortions and adversarial removal attacks even on the generated outputs of those finetuned models.