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CLC number: TP391

On-line Access: 2016-12-13

Received: 2015-09-10

Revision Accepted: 2016-02-21

Crosschecked: 2016-11-08

Cited: 0

Clicked: 3673

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Mohammad Mosleh

http://orcid.org/0000-0002- 0991-1623

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.12 P.1320-1330

http://doi.org/10.1631/FITEE.1500297


A robust intelligent audio watermarking scheme using support vector machine


Author(s):  Mohammad Mosleh, Hadi Latifpour, Mohammad Kheyrandish, Mahdi Mosleh, Najmeh Hosseinpour

Affiliation(s):  Department of Computer Engineering, Dezfoul Branch, Islamic Azad University, Dezfoul, Iran; more

Corresponding email(s):   Mosleh@iaud.ac.ir

Key Words:  Audio watermarking, Copyright protection, Singular value decomposition (SVD), Machine learning, Support vector machine (SVM)


Mohammad Mosleh, Hadi Latifpour, Mohammad Kheyrandish, Mahdi Mosleh, Najmeh Hosseinpour. A robust intelligent audio watermarking scheme using support vector machine[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(12): 1320-1330.

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Abstract: 
Rapid growth in information technology and computer networks has resulted in the universal use of data transmission in the digital domain. However, the major challenge faced by digital data owners is protection of data against unauthorized copying and distribution. Digital watermark technology is starting to be considered a credible protection method to mitigate the potential challenges that undermine the efficiency of the system. Digital audio watermarking should retain the quality of the host signal in a way that remains inaudible to the human hearing system. It should be sufficiently robust to be resistant against potential attacks. One of the major deficiencies of conventional audio watermarking techniques is the use of non-intelligent decoders in which some sets of specific rules are used for watermark extraction. This paper presents a new robust intelligent audio watermarking scheme using a synergistic combination of singular value decomposition (SVD) and support vector machine (SVM). The methodology involves embedding a watermark data by modulating the singular values in the SVD transform domain. In the extraction process, an intelligent detector using SVM is suggested for extracting the watermark data. By learning the destructive effects of noise, the detector in question can effectively retrieve the watermark. Diverse experiments under various conditions have been carried out to verify the performance of the proposed scheme. Experimental results showed better imperceptibility, higher robustness, lower payload, and higher operational efficiency, for the proposed method than for conventional techniques.

In this manuscript, a novel audio watermarking technique is proposed. The introduced methodology is making use of the SVD domain to hide the watermark information and the SVMs to detect the watermark bits. The technical presentation of the manuscript is adequate, while the experimental analysis is satisfactory. The trade-off between the watermarking properties is quite noteworthy.

运用支持向量机的稳健智能音频水印设计

概要:信息技术和计算机网络的快速发展引发了数字域数据传输的广泛使用。然而,如何保护数据免于非授权复制与分发行为也是数据所有者们所面临的主要挑战。数字水印技术作为缓解导致系统效率下降潜在挑战一种可靠保护方法,逐渐被人们所认同数字音频水印应当能以人耳不能察觉的方式保持主信号的质量,也应当能在潜在的攻击前保持足够的稳健型。传统音频水印技术存在的一个主要问题是使用非智能解码器--此类解码器在提取数字水印时仅使用特定的规则集。本文提出了一种稳健、智能的音频水印方法,该方法有效地结合了奇异值分解(Singular value decomposition, SVD)和支持向量机(Support vector machine,SVM)技术。该方法通过调整奇异值实现水印数据嵌入,又通过SVM智能解码器实现水印提取。此外,通过学习噪声信号的有害效应,该解码器能够有效的提取水印。不同条件下的一系列实验验证了所述设计的性能。实验结果表明,与传统方法相比,本文方法能够提供更好的不可见性、更高的鲁棒性、更低的负载和更高的操作效率。

关键词:音频水印;版权保护;奇异值分解;机器学习;支持向量机

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