IDEAW: 具有可逆雙嵌入的強健神經音頻水印技術

IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding

September 29, 2024

作者: Pengcheng Li, Xulong Zhang, Jing Xiao, Jianzong Wang

cs.AI

摘要

音頻水印技術將訊息嵌入音頻中，並能準確地從帶水印的音頻中提取訊息。傳統方法基於專家經驗開發演算法，將水印嵌入信號的時域或變換域中。隨著深度神經網絡的發展，基於深度學習的神經音頻水印技術應運而生。與傳統演算法相比，神經音頻水印技術在訓練過程中考慮各種攻擊，實現更好的魯棒性。然而，當前的神經水印技術存在容量較低和感知性不佳的問題。此外，在神經音頻水印技術中更加突出的水印定位問題尚未得到充分研究。本文設計了一個雙嵌入水印模型以實現高效的定位。我們還考慮了攻擊層對可逆神經網絡在魯棒性訓練中的影響，改進模型以提高其合理性和穩定性。實驗表明，所提出的IDEAW模型相較於現有方法，具有更高的容量和更高效的定位能力，能夠抵禦各種攻擊。

English

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.