# WaveVerify: A Novel Audio Watermarking Framework for Media Authentication and Combatting Deepfakes

arXiv: https://arxiv.org/abs/2507.21150 · HTML: https://mlsanity.com/paper/2507.21150/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 19 of 19)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [High-Fidelity Audio Compression with Improved RVQGAN](https://mlsanity.com/paper/2306.06546/) (2023)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision](https://mlsanity.com/paper/2302.03540/) (2023)
- [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)
- [Translatotron 2: High-quality direct speech-to-speech translation with voice preservation](https://mlsanity.com/paper/2107.08661/) (2021)
- [WavMark: Watermarking for Audio Generation](https://mlsanity.com/paper/2308.12770/) (2023)
- [SEANet: A Multi-modal Speech Enhancement Network](https://mlsanity.com/paper/2009.02095/) (2020)
- [XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection](https://mlsanity.com/paper/2411.10027/) (2024)
- [AudioMarkBench: Benchmarking Robustness of Audio Watermarking](https://mlsanity.com/paper/2406.06979/) (2024)
- [DeAR: A Deep-learning-based Audio Re-recording Resilient Watermarking](https://mlsanity.com/paper/2212.02339/) (2022)
- [SilentCipher: Deep Audio Watermarking](https://mlsanity.com/paper/2406.03822/) (2024)
- [DiPCo -- Dinner Party Corpus](https://mlsanity.com/paper/1909.13447/) (2019)
- [An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio](https://mlsanity.com/paper/2208.09646/) (2022)
- [SoK: How Robust is Audio Watermarking in Generative AI models?](https://mlsanity.com/paper/2503.19176/) (2025)
- [IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding](https://mlsanity.com/paper/2409.19627/) (2024)

## Built on by (most cited first, 3 of 3)

- [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)
- [VoxMorph: Scalable Zero-shot Voice Identity Morphing via Disentangled Embeddings](https://mlsanity.com/paper/2601.20883/) (2026)

## Abstract (arXiv)

The rapid advancement of voice generation technologies has enabled the synthesis of speech that is perceptually indistinguishable from genuine human voices. While these innovations facilitate beneficial applications such as personalized text-to-speech systems and voice preservation, they have also introduced significant risks, including deepfake impersonation scams and synthetic media-driven disinformation campaigns. Recent reports indicate that in 2024, deepfake fraud attempts surged by over 1,300% compared to 2023, underscoring the urgent need for robust audio content authentication. The financial sector has been particularly impacted, with a loss of over 10 million USD to voice scams and individual victims reporting losses exceeding 6,000 from AI-generated deepfake calls. In response, regulators and governments worldwide are enacting measures to improve AI content transparency and traceability, emphasizing the development of forensic tools and watermarking techniques as essential strategies to uphold media integrity.