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.

翻译：音频水印技术将信息嵌入音频中，并能从含水印音频中准确提取信息。传统方法基于专家经验设计算法，将水印嵌入信号的时域或变换域。随着深度神经网络的发展，基于深度学习的神经音频水印技术应运而生。与传统算法相比，神经音频水印通过在训练中考虑多种攻击，实现了更好的鲁棒性。然而，现有神经水印方法存在容量低、不可感知性不理想的问题。此外，水印定位这一在神经音频水印中极为重要且更为突出的问题尚未得到充分研究。本文设计了一种双嵌入水印模型以实现高效定位。我们同时考虑了鲁棒性训练中攻击层对可逆神经网络的影响，通过改进模型提升了其合理性与稳定性。实验表明，与现有方法相比，所提出的IDEAW模型能以更高容量和更高效的定位能力抵御多种攻击。