IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding
The audio watermarking technique embeds messages into audio and accurately
extracts messages from the watermarked audio. Traditional methods develop
algorithms based on expert experience to embed watermarks into the time-domain
or transform-domain of signals. With the development of deep neural networks,
deep learning-based neural audio watermarking has emerged. Compared to
traditional algorithms, neural audio watermarking achieves better robustness by
considering various attacks during training. However, current neural
watermarking methods suffer from low capacity and unsatisfactory
imperceptibility. Additionally, the issue of watermark locating, which is
extremely important and even more pronounced in neural audio watermarking, has
not been adequately studied. In this paper, we design a dual-embedding
watermarking model for efficient locating. We also consider the impact of the
attack layer on the invertible neural network in robustness training, improving
the model to enhance both its reasonableness and stability. Experiments show
that the proposed model, IDEAW, can withstand various attacks with higher
capacity and more efficient locating ability compared to existing methods.
66fb94e5524b58795e7adc28
Pengcheng Li
claimed_verified
2024-10-01T09:13:43.053Z
66fbba9052eeec12b1915e7d
/avatars/317fcc57290686094050bf97fdcc6b49.svg
Pechola Lee
user
Pechola
66fb94e5524b58795e7adc29
Xulong Zhang
extracted_confirmed
2024-10-01T06:25:15.435Z
64631720d9b490218812ef63
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/6qXagHvh58pQbq9RK9klE.png
Xulong Zhang
user
zhangxulong
66fb94e5524b58795e7adc2a
Jing Xiao
66fb94e5524b58795e7adc2b
Jianzong Wang
IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding
The audio watermarking technique embeds messages into audio and accurately
extracts messages from the watermarked audio. Traditional methods develop
algorithms based on expert experience to embed watermarks into the time-domain
or transform-domain of signals. With the development of deep neural networks,
deep learning-based neural audio watermarking has emerged. Compared to
traditional algorithms, neural audio watermarking achieves better robustness by
considering various attacks during training. However, current neural
watermarking methods suffer from low capacity and unsatisfactory
imperceptibility. Additionally, the issue of watermark locating, which is
extremely important and even more pronounced in neural audio watermarking, has
not been adequately studied. In this paper, we design a dual-embedding
watermarking model for efficient locating. We also consider the impact of the
attack layer on the invertible neural network in robustness training, improving
the model to enhance both its reasonableness and stability. Experiments show
that the proposed model, IDEAW, can withstand various attacks with higher
capacity and more efficient locating ability compared to existing methods.
A dual-embedding neural audio watermarking model enhances robustness and efficient locating under various attacks compared to existing methods.
deep learning-based neural audio watermarking transform-domain deep neural networks neural audio watermarking robustness watermark locating invertible neural network IDEAW