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Article

Watermarking Based on Compressive Sensing for Digital Speech Detection and Recovery

1
School of Computer Software, Tianjin University, Tianjin 300350, China
2
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
3
School of Computer Science and Technology, Qinghai Nationalities University, Xining 810007, China
4
School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
5
School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292, Japan
*
Author to whom correspondence should be addressed.
This paper is an extension of our paper published in Li, J.; Lu, W.; Zhang, C.; Wei, J.; Cao, X.; Dang, J. A Study on Detection and Recovery of Speech Signal Tampering. In Proceedings of the IEEE Trustcom/BigDatase/ISPA, Tianjin, China, 23–26 August 2016; pp. 678–682.
Sensors 2018, 18(7), 2390; https://doi.org/10.3390/s18072390
Received: 18 May 2018 / Revised: 9 July 2018 / Accepted: 14 July 2018 / Published: 23 July 2018
(This article belongs to the Special Issue Advances on Resources Management for Multi-Platform Infrastructures)
In this paper, a novel imperceptible, fragile and blind watermark scheme is proposed for speech tampering detection and self-recovery. The embedded watermark data for content recovery is calculated from the original discrete cosine transform (DCT) coefficients of host speech. The watermark information is shared in a frames-group instead of stored in one frame. The scheme trades off between the data waste problem and the tampering coincidence problem. When a part of a watermarked speech signal is tampered with, one can accurately localize the tampered area, the watermark data in the area without any modification still can be extracted. Then, a compressive sensing technique is employed to retrieve the coefficients by exploiting the sparseness in the DCT domain. The smaller the tampered the area, the better quality of the recovered signal is. Experimental results show that the watermarked signal is imperceptible, and the recovered signal is intelligible for high tampering rates of up to 47.6%. A deep learning-based enhancement method is also proposed and implemented to increase the SNR of recovered speech signal. View Full-Text
Keywords: digital watermarking; self-recovery; speech detection; discrete cosine transform; compressive sensing digital watermarking; self-recovery; speech detection; discrete cosine transform; compressive sensing
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MDPI and ACS Style

Lu, W.; Chen, Z.; Li, L.; Cao, X.; Wei, J.; Xiong, N.; Li, J.; Dang, J. Watermarking Based on Compressive Sensing for Digital Speech Detection and Recovery . Sensors 2018, 18, 2390. https://doi.org/10.3390/s18072390

AMA Style

Lu W, Chen Z, Li L, Cao X, Wei J, Xiong N, Li J, Dang J. Watermarking Based on Compressive Sensing for Digital Speech Detection and Recovery . Sensors. 2018; 18(7):2390. https://doi.org/10.3390/s18072390

Chicago/Turabian Style

Lu, Wenhuan, Zonglei Chen, Ling Li, Xiaochun Cao, Jianguo Wei, Naixue Xiong, Jian Li, and Jianwu Dang. 2018. "Watermarking Based on Compressive Sensing for Digital Speech Detection and Recovery " Sensors 18, no. 7: 2390. https://doi.org/10.3390/s18072390

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