wavmark 0.0.2

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.

Installation

pip install wavmark

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.read host audio
# the audio should be a single-channel 16kHz wav, you can read it using soundfile:
signal, sample_rate = soundfile.read("example.wav")
# Otherwise, you can use the following function to convert the host audio to single-channel 16kHz format:
# from wavmark.utils import file_reader
# signal = file_reader.read_as_single_channel("example.wav", aim_sr=16000)

# 4.encode watermark
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)

# 5.decode watermark
payload_decoded, _ = wavmark.decode_watermark(model, watermarked_signal, show_progress=True)
BER = (payload != payload_decoded).mean() * 100

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 same watermark is added repetitively 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.

Low-level API

For a watermarking algorithm, there exists a trade-off among its capacity, robustness, and imperceptibility. Therefore, watermarking system often needs customization according to actual application requirements. The good news is that WavMark is entirely implemented with PyTorch. You can leverage the PyTorch model to construct your own watermarking system. Here is an example of directly calling the PyTorch model:

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

# 2. take 16,000 samples
signal, sample_rate = soundfile.read("example.wav")
trunck = signal[0:16000]
message_npy = np.random.choice([0, 1], size=32)

# 3. do encode:
with torch.no_grad():
    signal = torch.FloatTensor(trunck).to(device)[None]
    message_tensor = torch.FloatTensor(message_npy).to(device)[None]
    signal_wmd_tensor = model.encode(signal, message_tensor)
    signal_wmd_npy = signal_wmd_tensor.detach().cpu().numpy().squeeze()

# 4.do decode:
with torch.no_grad():
    signal = torch.FloatTensor(signal_wmd_npy).to(device).unsqueeze(0)
    message_decoded_npy = (model.decode(signal) >= 0.5).int().detach().cpu().numpy().squeeze()

BER = (message_npy != message_decoded_npy).mean() * 100
print("BER:", BER)

Thanks

The "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}
}