wavmark 0.0.1

AI-Based Audio Watermarking Tool

WavMark

AI-based Audio Watermarking Tool

• ⚡ Leading Stability: The watermark resist to 10 types of common attacks like Gaussian noise, MP3 compression, high-pass filter, and speed variation; achieving over 29 times in robustness compared with the traditional method.
• 🙉 High Imperceptibility: The watermarked audio has over 38dB SNR and 4.3 PESQ, which means it is inaudible to humans. Listen to our demo: https://wavmark.github.io/.
• 😉 Easy for Extending: This project is entirely python based. You can easily leverage our underlying PyTorch model to implement a custom watermarking system with higher capacity and robustness.

Basic Usage

The following code adds 16-bit watermark into the input file example.wav and subsequently performs decoding:

import numpy as np
import soundfile
import torch
import wavmark

# 1.load model
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = wavmark.load_model().to(device)

# 2.create 16-bit payload
payload = np.random.choice([0, 1], size=16)
print("Payload:", payload)

# 3.encode watermark
signal, sample_rate = soundfile.read("example.wav")
watermarked_signal, _ = wavmark.encode_watermark(model, signal, payload, show_progress=True)
# You can save it as a new wav:
soundfile.write("output.wav", watermarked_signal, 16000)

# 4.decode watermark
payload_decoded, _ = wavmark.decode_watermark(model, watermarked_signal, show_progress=True)

BER = 100 * (1 - (payload == payload_decoded).mean())

print("Decode BER:%.1f" % BER)

How it works?

In paper WavMark: Watermarking for Audio Generation we proposed the WavMark model, which enables encoding 32 bits of information into 1-second audio. In this tool, we take the first 16 bits as a fixed pattern for watermark identification and the remaining 16 bits as a custom payload. The watermark is added iteratively into the host to ensure full-time region protection:

Since the pattern length is 16, the probability of "mistakenly identifying an unwatermarked audio as watermarked" is only 1/(2^16)=0.000015.

Thanks

The audio watermarking tool "Audiowmark" developed by Stefan Westerfeld has provided valuable ideas for the design of this project.

Citation

@misc{chen2023wavmark,
      title={WavMark: Watermarking for Audio Generation}, 
      author={Guangyu Chen and Yu Wu and Shujie Liu and Tao Liu and Xiaoyong Du and Furu Wei},
      year={2023},
      eprint={2308.12770},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}