IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding
Authors: name: Pengcheng Li, name: Xulong Zhang, name: Jing Xiao, name: Jianzong Wang

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.