# SilentCipher: Deep Audio Watermarking

arXiv: https://arxiv.org/abs/2406.03822 · HTML: https://mlsanity.com/paper/2406.03822/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2024.

## Built on by (most cited first, 21 of 21)

- [Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling](https://mlsanity.com/paper/2509.08753/) (2025)
- [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)
- [Audio Codec Augmentation for Robust Collaborative Watermarking of Speech Synthesis](https://mlsanity.com/paper/2409.13382/) (2024)
- [Self Voice Conversion as an Attack against Neural Audio Watermarking](https://mlsanity.com/paper/2601.20432/) (2026)
- [Watermarking for AI Content Detection: A Review on Text, Visual, and Audio Modalities](https://mlsanity.com/paper/2504.03765/) (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)
- [The Impact of Audio Watermarking on Audio Anti-Spoofing Countermeasures](https://mlsanity.com/paper/2509.20736/) (2025)
- [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)
- [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)
- [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)
- [MerkleSpeech: Public-Key Verifiable, Chunk-Localised Speech Provenance via Perceptual Fingerprints and Merkle Commitments](https://mlsanity.com/paper/2602.10166/) (2026)
- [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)
- [Tokens Change, Structure Endures: Spectral Watermarking for Generated Speech](https://mlsanity.com/paper/2609.33774/) (2026)

## Abstract (arXiv)

In the realm of audio watermarking, it is challenging to simultaneously encode imperceptible messages while enhancing the message capacity and robustness. Although recent advancements in deep learning-based methods bolster the message capacity and robustness over traditional methods, the encoded messages introduce audible artefacts that restricts their usage in professional settings. In this study, we introduce three key innovations. Firstly, our work is the first deep learning-based model to integrate psychoacoustic model based thresholding to achieve imperceptible watermarks. Secondly, we introduce psuedo-differentiable compression layers, enhancing the robustness of our watermarking algorithm. Lastly, we introduce a method to eliminate the need for perceptual losses, enabling us to achieve SOTA in both robustness as well as imperceptible watermarking. Our contributions lead us to SilentCipher, a model enabling users to encode messages within audio signals sampled at 44.1kHz.