Presenting an intelligent extraction method in audio watermarking systems based on lifting wavelet transform and support vector machine

Document Type : Persian Original Article

Authors

1 Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

2 Department of Electrical & Computer Engineering, AEL Group, McMaster University, Hamilton, ON, Canada

Abstract

Nowadays, the rapid growth of the use of information technology and computer networks has increased the transfer of information in a digital form. For this reason, the protection of data has become one of the most important challenges in this field. Watermarking is introduced as one of the newest and most important techniques for data protection. Audio watermarking is considered to be the most challenging type of watermarking due to the nature of audio files. The most extraction methods used in audio watermarking algorithms, which mainly use non-intelligent techniques based on the reverse of embedding rules for extraction phase of audio watermarking, often they are not able to extract watermarking exactly and have a lot of errors in extracting. Our proposed solution to solve this problem is to use an intelligent algorithm to extract the watermark. The purpose of this article is to provide a method that covered the weakness of non-intelligent extraction methods using trained machine learning classifier and helped to improve system performance. For the embedding operation, the Lifting Wavelet Transform (LWT) has been used in the proposed method; in the extraction operation, the Support Vector Machine (SVM) classifier is also used. The trained classifier is able to detect the effects of various attacks on the audio files and consequently, intelligent and precise extraction of watermark. The results of various experiments under different conditions indicate that this intelligent method has achieved appropriate imperceptibility and high capacity along with high robustness.

Keywords

References

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Journal of Soft Computing and Information Technology
Volume 9, Issue 2 - Serial Number 28
Summer
July 2020
Pages 34-47

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