Audio watermarking aims to embed identifiable information into audio while remaining imperceptible. Existing methods adopt high-fidelity, low-energy designs to preserve perceptual quality, but the resulting watermarks lack robustness under suppression by speech reconstruction models. Improving robustness is challenging due to the inherent robustness-fidelity trade-off in existing designs, where increasing watermark energy improves robustness but reduces fidelity.
To address this problem, we propose a feature-aligned watermarking method that aligns the watermark with the original speech feature distribution, allowing higher watermark energy to improve robustness while preserving imperceptibility. We use a pretrained speech codec to generate a pseudo-speech watermark and fuse it into the spectrogram of the input audio, with VAD loss and perceptual losses guiding embedding within voiced regions. Experiments show that our method maintains imperceptibility comparable to existing approaches while substantially improving robustness under both seen and unseen speech reconstruction models.
Highlights
Feature-aligned watermark design for improved robustness under reconstruction-based suppression
Pseudo-speech watermark generation with a pretrained speech codec
Voiced-region embedding guided by VAD and perceptual objectives
Requirements
Python 3.11 or later
Install dependencies with:
pip install -r requirements.txt
Quick Start
Embed a watermark
bash
1python -m main embed \2 --input ./example.wav \3 --output ./outputs/watermarked.wav \4 --message 1111001110101001\5 --device cpu
Decode a watermark
bash
1python -m main decode \2 --input ./outputs/watermarked.wav \3 --label 1111001110101001\4 --device cpu
Repository Structure
main.py — command-line entry point
inferencer.py — end-to-end embedding and detection pipeline
base.py — model loading, checkpoint handling, and device setup
models.py — watermarking model components
util.py — helper utilities for message generation and serialization
speechtokenizer/ — external dependency with pretrained codec components
weight.pth — released checkpoint
example.wav — sample audio for quick testing
outputs/ — generated output files
License
This project is released under the Apache 2.0 License.
