A learning-based audio watermarking scheme using kernel Fisher discriminant analysis

Authors:
Hong Peng;Bing Li;Xiaohui Luo;Jun Wang;Zulin Zhang
Affiliations:
School of Mathematics and Computer Engineering, Xihua University, Chengdu, Sichuan 610039, China;School of Mathematics and Computer Engineering, Xihua University, Chengdu, Sichuan 610039, China;School of Mathematics and Computer Engineering, Xihua University, Chengdu, Sichuan 610039, China;School of Electrical and Information Engineering, Xihua University, Chengdu, Sichuan 610039, China;Department of Computer Science, Sichuan University for Nationalities, Kangding, Sichuan 626001, China
Venue:
Digital Signal Processing
Year:
2013

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Abstract

A novel learning-based audio watermarking scheme using kernel Fisher discriminant analysis (KFDA) is proposed in this paper. Two techniques, down-sampling technique and energy relationship modulation technique, are developed in order to guarantee good fidelity of the watermarked audio signal. At the same time, local energy relationship between audio sub-frames is hid in the watermarked audio signal with watermark embedding. Moreover, a learning-based watermark detector using the KFDA is exploited and it extracts the watermark by learning the local energy relationship hid in the watermarked audio signal. Due to powerful non-linear learning ability and good generalization ability of the KFDA, the learning-based watermark detector can exhibit high robustness against common audio signal processing or attacks compared with other audio watermarking methods. In addition, it also has simple implementation and lower computation complexity.