A Novel Robust Zero-Watermarking Algorithm for Audio Based on Sparse Representation

查看全文 作　　者：Longting [1]Xu;Daiyu [1]Huang;Xing [1]Guo;Wei [2]Rao;Yunyun [2]Ji;Ruoyi [1]Li;Xiaochen [1]Lu

机构地区：[1]College of Information Science and Technology,Donghua University,Shanghai 200000,China;[2]Tencent Ethereal Audio Lab,China

出　　处：《China Communications》索引2021年第18卷第8期,共12页

基　　金：the National Natural Science Foundation of China(No.62001100);the Fundamental Research Funds for the Central Universities(No.2232019D3-52);Shanghai Sailing Program.(No.19YF1402000). 摘　　要：Behind the prevalence of multimedia technology,digital copyright disputes are becoming increasingly serious.The digital watermarking prevention technique against the copyright infringement needs to be improved urgently.Among the proposed technologies,zero-watermarking has been favored recently.In order to improve the robustness of the zero-watermarking,a novel robust audio zerowatermarking method based on sparse representation is proposed.The proposed scheme is mainly based on the K-singular value decomposition(K-SVD)algorithm to construct an optimal over complete dictionary from the background audio signal.After that,the orthogonal matching pursuit(OMP)algorithm is used to calculate the sparse coefficient of the segmented test audio and generate the corresponding sparse coefficient matrix.Then,the mean value of absolute sparse coefficients in the sparse matrix of segmented speech is calculated and selected,and then comparing the mean absolute coefficient of segmented speech with the average value of the selected coefficients to realize the embedding of zero-watermarking.Experimental results show that the proposed audio zerowatermarking algorithm based on sparse representation performs effectively in resisting various common attacks.Compared with the baseline works,the proposed method has better robustness. 关键词：ZERO-WATERMARKING K-singular value decomposition dictionary learning sparse representtion

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A Novel Robust Zero-Watermarking Algorithm for Audio Based on Sparse Representation

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