Digital audio watermark detection method based on multi-parameter Weibull statistical modeling

Abstract

The invention discloses a digital audio watermark detection method based on multi-parameter Weibull statistical modeling. The watermark embedding comprises firstly selecting an SWT high-frequency sub-band with large host audio energy as an optimal sub-band for watermark embedding, modifying the coefficient of the optimal sub-band in a multiplicative embedding way, and merging the sub-band with thecoefficient modified and other sub-bands to perform SWT reconstruction to obtain a watermark-containing audio file. The watermark extraction comprises subjecting the watermark-containing audio file to SWT at a receiving end, modeling the high-frequency sub-band significant coefficient of the watermark-containing audio file by using multi-parameter weibull distribution, and estimating parameters of the model by using an in-sub-band coefficient correlation. Finally, by using a maximum likelihood and local maximum potential energy test method, a local optimal detector is designed to extract specific watermark information, and the specific watermark information is sorted to obtain a final watermark sequence.

Description

technical field [0001] The invention belongs to the technical field of copyright protection of digital audio, relates to a digital audio watermarking method based on a statistical model, in particular to a digital watermark detection method based on multi-parameter Weibull statistical modeling. Background technique [0002] In today's information age, people can more easily obtain digital multimedia resources such as audio, video and images through mobile devices and Internet technology. As an important part of multimedia resources, digital audio is becoming more and more easy to be illegally copied, tampered with and disseminated, which makes the information security of audio more serious. In order to protect the legitimate rights and interests of audio copyright owners, it is especially important to add identity verification information in audio, and digital audio watermarking technology is one of the most effective methods to achieve this purpose. [0003] In recent year...

AI Technical Summary

Problems solved by technology

However, watermarking methods based on statistical models still have the following shortcomings: first, the transform domains currently used are usually downsampled wavelet transform, discrete cosine transform, and Fourier transform, which cannot capture important information and features of audio well; second , when modeling the transform domain coefficients, there is no in-depth analysis of the selected distribution model, and it is not fully proved whether the established model is optimal for the selected transform domain; third, when the model distribution parameters are estimated, the watermarked audio signal is directly Parameter estimation is greatly affected by the watermark embedding strength; fourth, most of the existing methods use the log likelihood ratio to construct a maximum likelihood detector, and do not try to use other statistical testing strategies to construct a new detector to improve watermark detection. precision

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