A proposed pattern recognition framework for EEG-based smart blind watermarking system

Duy Pham, Dat Tran, Wanli Ma

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

Copyright protection for multimedia data owners is of crucial importance as the duplication of multimedia data has become easily with the advent of Internet and digital multimedia technology. Current digital watermarking techniques for preserving the product ownership are rule-based and not directly deal with the data synchronization, therefore their decoding performance reduces significantly when the watermarked data is transmitted through a real communication channel. This paper proposes a pattern recognition framework to build a new blind watermark scheme for electroencephalography (EEG) data. Embedding a watermark is based on modifying mean modulation relationship of approximation coefficient in wavelet domain. Retrieving this watermark is done effectively using Support vector data description (SVDD) models trained with the correlation between modified frequency coefficients and the watermark sequence in wavelet domain. Experimental results show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as random cropping, noise addition, low-pass filtering, and resampling.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages955-960
Number of pages6
ISBN (Electronic)9781509048472
ISBN (Print)9781509048489
DOIs
Publication statusPublished - 4 Dec 2016
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016

Conference

Conference23rd International Conference on Pattern Recognition, ICPR 2016
CountryMexico
CityCancun
Period4/12/168/12/16

Fingerprint

Data description
Digital watermarking
Watermarking
Electroencephalography
Pattern recognition
Decoding
Synchronization
Signal processing
Modulation
Internet

Cite this

Pham, D., Tran, D., & Ma, W. (2016). A proposed pattern recognition framework for EEG-based smart blind watermarking system. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (pp. 955-960). [7899759] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICPR.2016.7899759
Pham, Duy ; Tran, Dat ; Ma, Wanli. / A proposed pattern recognition framework for EEG-based smart blind watermarking system. 2016 23rd International Conference on Pattern Recognition, ICPR 2016. IEEE, Institute of Electrical and Electronics Engineers, 2016. pp. 955-960
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abstract = "Copyright protection for multimedia data owners is of crucial importance as the duplication of multimedia data has become easily with the advent of Internet and digital multimedia technology. Current digital watermarking techniques for preserving the product ownership are rule-based and not directly deal with the data synchronization, therefore their decoding performance reduces significantly when the watermarked data is transmitted through a real communication channel. This paper proposes a pattern recognition framework to build a new blind watermark scheme for electroencephalography (EEG) data. Embedding a watermark is based on modifying mean modulation relationship of approximation coefficient in wavelet domain. Retrieving this watermark is done effectively using Support vector data description (SVDD) models trained with the correlation between modified frequency coefficients and the watermark sequence in wavelet domain. Experimental results show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as random cropping, noise addition, low-pass filtering, and resampling.",
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Pham, D, Tran, D & Ma, W 2016, A proposed pattern recognition framework for EEG-based smart blind watermarking system. in 2016 23rd International Conference on Pattern Recognition, ICPR 2016., 7899759, IEEE, Institute of Electrical and Electronics Engineers, pp. 955-960, 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, 4/12/16. https://doi.org/10.1109/ICPR.2016.7899759

A proposed pattern recognition framework for EEG-based smart blind watermarking system. / Pham, Duy; Tran, Dat; Ma, Wanli.

2016 23rd International Conference on Pattern Recognition, ICPR 2016. IEEE, Institute of Electrical and Electronics Engineers, 2016. p. 955-960 7899759.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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AU - Tran, Dat

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N2 - Copyright protection for multimedia data owners is of crucial importance as the duplication of multimedia data has become easily with the advent of Internet and digital multimedia technology. Current digital watermarking techniques for preserving the product ownership are rule-based and not directly deal with the data synchronization, therefore their decoding performance reduces significantly when the watermarked data is transmitted through a real communication channel. This paper proposes a pattern recognition framework to build a new blind watermark scheme for electroencephalography (EEG) data. Embedding a watermark is based on modifying mean modulation relationship of approximation coefficient in wavelet domain. Retrieving this watermark is done effectively using Support vector data description (SVDD) models trained with the correlation between modified frequency coefficients and the watermark sequence in wavelet domain. Experimental results show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as random cropping, noise addition, low-pass filtering, and resampling.

AB - Copyright protection for multimedia data owners is of crucial importance as the duplication of multimedia data has become easily with the advent of Internet and digital multimedia technology. Current digital watermarking techniques for preserving the product ownership are rule-based and not directly deal with the data synchronization, therefore their decoding performance reduces significantly when the watermarked data is transmitted through a real communication channel. This paper proposes a pattern recognition framework to build a new blind watermark scheme for electroencephalography (EEG) data. Embedding a watermark is based on modifying mean modulation relationship of approximation coefficient in wavelet domain. Retrieving this watermark is done effectively using Support vector data description (SVDD) models trained with the correlation between modified frequency coefficients and the watermark sequence in wavelet domain. Experimental results show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as random cropping, noise addition, low-pass filtering, and resampling.

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Pham D, Tran D, Ma W. A proposed pattern recognition framework for EEG-based smart blind watermarking system. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016. IEEE, Institute of Electrical and Electronics Engineers. 2016. p. 955-960. 7899759 https://doi.org/10.1109/ICPR.2016.7899759