A proposed blind DWT-SVD watermarking scheme for EEG data

Duy Pham, Dat TRAN, Wanli MA

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

4 Citations (Scopus)
2 Downloads (Pure)

Abstract

Copyright and integrity violations of digital medical data have become security challenges since the ever-increasing distribution of them between clinical centres and hospitals through the widespread usage of telemedicine, teleradiology, telediagnosis, and teleconsultation. Therefore, preserving authenticity and integrity of medical data including Electroencephalogram (EEG) has become a necessity. Watermark techniques have been thoroughly studied as a means to preserve the authenticity and integrity of the content of medical. Although there is a large volume of works on watermarking and stenography, not many researchers have addressed issues related to EEG data. This paper proposes a new approach that uses discrete wavelet transform (DWT) to decompose EEG signals and singular value decomposition (SVD) to embed watermark into the decomposed EEG signal. Based on the advantage of using the SVD technique, our proposed method achieved blind detection of watermark in which the receiver does not require the original EEG signal to retrieve the watermark. Experimental results show that the proposed EEG watermarking approach maintains the high quality of the EEG signal.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2015)
Subtitle of host publicationLecture notes in computer science
EditorsSabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu
Place of PublicationCham, Switzerland
PublisherSpringer
Pages69-76
Number of pages8
Volume9492
ISBN (Electronic)9783319265612
ISBN (Print)9783319265605
DOIs
Publication statusPublished - 2015
Event22nd International Conference on Neural Information Processing ICONIP 2015 - Istanbul, Istanbul, Turkey
Duration: 9 Nov 201512 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9492
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Neural Information Processing ICONIP 2015
Abbreviated titleICONIP 2015
CountryTurkey
CityIstanbul
Period9/11/1512/11/15

Fingerprint

Discrete wavelet transforms
Watermarking
Singular value decomposition
Electroencephalography
Telemedicine

Cite this

Pham, D., TRAN, D., & MA, W. (2015). A proposed blind DWT-SVD watermarking scheme for EEG data. In S. Arik, T. Huang, W. K. Lai, & Q. Liu (Eds.), International Conference on Neural Information Processing (ICONIP 2015): Lecture notes in computer science (Vol. 9492, pp. 69-76). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9492). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-26561-2_9
Pham, Duy ; TRAN, Dat ; MA, Wanli. / A proposed blind DWT-SVD watermarking scheme for EEG data. International Conference on Neural Information Processing (ICONIP 2015): Lecture notes in computer science. editor / Sabri Arik ; Tingwen Huang ; Weng Kin Lai ; Qingshan Liu. Vol. 9492 Cham, Switzerland : Springer, 2015. pp. 69-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{5c1bac4702944c80a5850e3481eb45b9,
title = "A proposed blind DWT-SVD watermarking scheme for EEG data",
abstract = "Copyright and integrity violations of digital medical data have become security challenges since the ever-increasing distribution of them between clinical centres and hospitals through the widespread usage of telemedicine, teleradiology, telediagnosis, and teleconsultation. Therefore, preserving authenticity and integrity of medical data including Electroencephalogram (EEG) has become a necessity. Watermark techniques have been thoroughly studied as a means to preserve the authenticity and integrity of the content of medical. Although there is a large volume of works on watermarking and stenography, not many researchers have addressed issues related to EEG data. This paper proposes a new approach that uses discrete wavelet transform (DWT) to decompose EEG signals and singular value decomposition (SVD) to embed watermark into the decomposed EEG signal. Based on the advantage of using the SVD technique, our proposed method achieved blind detection of watermark in which the receiver does not require the original EEG signal to retrieve the watermark. Experimental results show that the proposed EEG watermarking approach maintains the high quality of the EEG signal.",
keywords = "watermarking, EEG, EEG Watermarking DWT SVD",
author = "Duy Pham and Dat TRAN and Wanli MA",
year = "2015",
doi = "10.1007/978-3-319-26561-2_9",
language = "English",
isbn = "9783319265605",
volume = "9492",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "69--76",
editor = "Sabri Arik and Tingwen Huang and Lai, {Weng Kin} and Qingshan Liu",
booktitle = "International Conference on Neural Information Processing (ICONIP 2015)",
address = "Netherlands",

}

Pham, D, TRAN, D & MA, W 2015, A proposed blind DWT-SVD watermarking scheme for EEG data. in S Arik, T Huang, WK Lai & Q Liu (eds), International Conference on Neural Information Processing (ICONIP 2015): Lecture notes in computer science. vol. 9492, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9492, Springer, Cham, Switzerland, pp. 69-76, 22nd International Conference on Neural Information Processing ICONIP 2015, Istanbul, Turkey, 9/11/15. https://doi.org/10.1007/978-3-319-26561-2_9

A proposed blind DWT-SVD watermarking scheme for EEG data. / Pham, Duy; TRAN, Dat; MA, Wanli.

International Conference on Neural Information Processing (ICONIP 2015): Lecture notes in computer science. ed. / Sabri Arik; Tingwen Huang; Weng Kin Lai; Qingshan Liu. Vol. 9492 Cham, Switzerland : Springer, 2015. p. 69-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9492).

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

TY - GEN

T1 - A proposed blind DWT-SVD watermarking scheme for EEG data

AU - Pham, Duy

AU - TRAN, Dat

AU - MA, Wanli

PY - 2015

Y1 - 2015

N2 - Copyright and integrity violations of digital medical data have become security challenges since the ever-increasing distribution of them between clinical centres and hospitals through the widespread usage of telemedicine, teleradiology, telediagnosis, and teleconsultation. Therefore, preserving authenticity and integrity of medical data including Electroencephalogram (EEG) has become a necessity. Watermark techniques have been thoroughly studied as a means to preserve the authenticity and integrity of the content of medical. Although there is a large volume of works on watermarking and stenography, not many researchers have addressed issues related to EEG data. This paper proposes a new approach that uses discrete wavelet transform (DWT) to decompose EEG signals and singular value decomposition (SVD) to embed watermark into the decomposed EEG signal. Based on the advantage of using the SVD technique, our proposed method achieved blind detection of watermark in which the receiver does not require the original EEG signal to retrieve the watermark. Experimental results show that the proposed EEG watermarking approach maintains the high quality of the EEG signal.

AB - Copyright and integrity violations of digital medical data have become security challenges since the ever-increasing distribution of them between clinical centres and hospitals through the widespread usage of telemedicine, teleradiology, telediagnosis, and teleconsultation. Therefore, preserving authenticity and integrity of medical data including Electroencephalogram (EEG) has become a necessity. Watermark techniques have been thoroughly studied as a means to preserve the authenticity and integrity of the content of medical. Although there is a large volume of works on watermarking and stenography, not many researchers have addressed issues related to EEG data. This paper proposes a new approach that uses discrete wavelet transform (DWT) to decompose EEG signals and singular value decomposition (SVD) to embed watermark into the decomposed EEG signal. Based on the advantage of using the SVD technique, our proposed method achieved blind detection of watermark in which the receiver does not require the original EEG signal to retrieve the watermark. Experimental results show that the proposed EEG watermarking approach maintains the high quality of the EEG signal.

KW - watermarking

KW - EEG

KW - EEG Watermarking DWT SVD

UR - http://www.scopus.com/inward/record.url?scp=84951873351&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-26561-2_9

DO - 10.1007/978-3-319-26561-2_9

M3 - Conference contribution

SN - 9783319265605

VL - 9492

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 69

EP - 76

BT - International Conference on Neural Information Processing (ICONIP 2015)

A2 - Arik, Sabri

A2 - Huang, Tingwen

A2 - Lai, Weng Kin

A2 - Liu, Qingshan

PB - Springer

CY - Cham, Switzerland

ER -

Pham D, TRAN D, MA W. A proposed blind DWT-SVD watermarking scheme for EEG data. In Arik S, Huang T, Lai WK, Liu Q, editors, International Conference on Neural Information Processing (ICONIP 2015): Lecture notes in computer science. Vol. 9492. Cham, Switzerland: Springer. 2015. p. 69-76. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-26561-2_9