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 language | English |
|---|---|
| Title of host publication | 2016 23rd International Conference on Pattern Recognition, ICPR 2016 |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 955-960 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509048472 |
| ISBN (Print) | 9781509048489 |
| DOIs | |
| Publication status | Published - 4 Dec 2016 |
| Event | 23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico Duration: 4 Dec 2016 → 8 Dec 2016 |
Conference
| Conference | 23rd International Conference on Pattern Recognition, ICPR 2016 |
|---|---|
| Country | Mexico |
| City | Cancun |
| Period | 4/12/16 → 8/12/16 |
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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 Book › Conference contribution
TY - GEN
T1 - A proposed pattern recognition framework for EEG-based smart blind watermarking system
AU - Pham, Duy
AU - Tran, Dat
AU - Ma, Wanli
PY - 2016/12/4
Y1 - 2016/12/4
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.
KW - Blind Watermarking
KW - EEG
KW - Support Vector Data Description
UR - http://www.scopus.com/inward/record.url?scp=85019059865&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2016.7899759
DO - 10.1109/ICPR.2016.7899759
M3 - Conference contribution
SN - 9781509048489
SP - 955
EP - 960
BT - 2016 23rd International Conference on Pattern Recognition, ICPR 2016
PB - IEEE, Institute of Electrical and Electronics Engineers
ER -