大数据2025，Vol.11Issue(4)：89-101,13.DOI:10.11959/j.issn.2096-0271.2025054
基于可逆网络双嵌入和攻击层的鲁棒音频水印方法
Invertible networks-based method with dual-embedding and attack layer for robust audio watermarking
摘要
Abstract
Digital audio watermarking aims to embed information into audio and accurately extract it from the watermarked audio.Traditional methods rely on algorithms designed based on expert experience to embed watermarks into the time domain or transform domain of signals.With the development of deep neural networks,neural audio watermarking based on deep learning has emerged.Compared with traditional algorithms,neural audio watermarking achieves better robustness by considering various attacks during training.However,current neural audio watermarking methods suffer from low capacity and poor imperceptibility.Furthermore,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 based on an invertible neural network for efficient localization.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 can withstand various attacks with higher capacity and more efficient locating ability compared with existing methods.关键词
神经音频水印/可逆神经网络/双嵌入/同步码/鲁棒性Key words
neural audio watermarking/invertible neural network/dual-embedding/synchronization code/robustness分类
信息技术与安全科学引用本文复制引用
张旭龙,瞿晓阳,李鹏程,肖春光,王健宗..基于可逆网络双嵌入和攻击层的鲁棒音频水印方法[J].大数据,2025,11(4):89-101,13.基金项目
广东省重点领域研发计划"新一代人工智能"重大专项(No.2021B0101400003) The Key Research and Development Program of Guangdong Province(No.2021B0101400003) （No.2021B0101400003）
