# AudioMarkBench: Benchmarking Robustness of Audio Watermarking

arXiv: https://arxiv.org/abs/2406.06979 · HTML: https://mlsanity.com/paper/2406.06979/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [HopSkipJumpAttack: A Query-Efficient Decision-Based Attack](https://mlsanity.com/paper/1904.02144/) (2019)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Evading Watermark based Detection of AI-Generated Content](https://mlsanity.com/paper/2305.03807/) (2023)
- [Proactive Detection of Voice Cloning with Localized Watermarking](https://mlsanity.com/paper/2401.17264/) (2024)
- [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)

## Built on by (most cited first, 23 of 23)

- [SoK: Watermarking for AI-Generated Content](https://mlsanity.com/paper/2411.18479/) (2024)
- [A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?](https://mlsanity.com/paper/2505.19663/) (2025)
- [SoK: How Robust is Audio Watermarking in Generative AI models?](https://mlsanity.com/paper/2503.19176/) (2025)
- [Watermarking Without Standards Is Not AI Governance](https://mlsanity.com/paper/2505.23814/) (2025)
- [AI-generated Image Detection: Passive or Watermark?](https://mlsanity.com/paper/2411.13553/) (2024)
- [XAttnMark: Learning Robust Audio Watermarking with Cross-Attention](https://mlsanity.com/paper/2502.04230/) (2025)
- [Self Voice Conversion as an Attack against Neural Audio Watermarking](https://mlsanity.com/paper/2601.20432/) (2026)
- [RoHOI: Robustness Benchmark for Human-Object Interaction Detection](https://mlsanity.com/paper/2507.09111/) (2025)
- [WaveVerify: A Novel Audio Watermarking Framework for Media Authentication and Combatting Deepfakes](https://mlsanity.com/paper/2507.21150/) (2025)
- [HarmonicAttack: An Adaptive Cross-Domain Audio Watermark Removal](https://mlsanity.com/paper/2511.21577/) (2025)
- [Measuring the Robustness of Audio Deepfake Detection under Real-World Corruption](https://mlsanity.com/paper/2503.17577/) (2025)
- [Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking](https://mlsanity.com/paper/2604.13776/) (2026)
- [Can DeepFake Speech be Reliably Detected?](https://mlsanity.com/paper/2410.06572/) (2024)
- [CloneShield: A Framework for Universal Perturbation Against Zero-Shot Voice Cloning](https://mlsanity.com/paper/2505.19119/) (2025)
- [Latent-Mark: An Audio Watermark Robust to Neural Codec Compression](https://mlsanity.com/paper/2603.05310/) (2026)
- [The Watermark Shortcut: How Provenance Marking Sabotages Audio Deepfake Detection](https://mlsanity.com/paper/2606.23335/) (2026)
- [AWARE: Audio Watermarking with Adversarial Resistance to Edits](https://mlsanity.com/paper/2510.17512/) (2025)
- [Audio Pirates: Black-box Audio Watermark Removal via Diffusion Priors](https://mlsanity.com/paper/2605.30614/) (2026)
- [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)
- [FeatMark: Feature-level Watermark Protection against Mimicry Attacks with Diffusion Models](https://mlsanity.com/paper/2609.30980/) (2026)
- [Tokens Change, Structure Endures: Spectral Watermarking for Generated Speech](https://mlsanity.com/paper/2609.33774/) (2026)
- [GAMW: Generation-Aware Mel-Domain Watermarking for TTS Traceability](https://mlsanity.com/paper/2610.15082/) (2026)

## Abstract (arXiv)

The increasing realism of synthetic speech, driven by advancements in text-to-speech models, raises ethical concerns regarding impersonation and disinformation. Audio watermarking offers a promising solution via embedding human-imperceptible watermarks into AI-generated audios. However, the robustness of audio watermarking against common/adversarial perturbations remains understudied. We present AudioMarkBench, the first systematic benchmark for evaluating the robustness of audio watermarking against watermark removal and watermark forgery. AudioMarkBench includes a new dataset created from Common-Voice across languages, biological sexes, and ages, 3 state-of-the-art watermarking methods, and 15 types of perturbations. We benchmark the robustness of these methods against the perturbations in no-box, black-box, and white-box settings. Our findings highlight the vulnerabilities of current watermarking techniques and emphasize the need for more robust and fair audio watermarking solutions. Our dataset and code are publicly available at https://github.com/moyangkuo/AudioMarkBench.