Damageless digital watermarking by machine learning: A method of key generation for information extraction using artificial neural networks

Kensuke Naoe; Hideyasu Sasaki; Yoshiyasu Takefuji

doi:10.1109/SoCPaR.2009.109

Damageless digital watermarking by machine learning: A method of key generation for information extraction using artificial neural networks

Kensuke Naoe, Hideyasu Sasaki, Yoshiyasu Takefuji

Faculty of Environment and Information Studies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Soft computing in the area of information security is a promising field for the creation of intelligent solutions. This paper discusses a method for digital watermarking using artificial neural networks to realize secure copyright protection of visual information without any damage. The discussed watermark extraction keys and feature extraction keys identify the secure and unique hidden patterns for proper digital watermarks. In the experiments, we have shown that the proposed method is robust to high pass filtering and JPEG compression of visual information, only for those watermark extraction keys which were able to identify the proper hidden bit patterns from original visual information using corresponding feature extraction keys. The proposed method is to contribute to secure visual digital watermarking without damaging or losing any detailed data of visual information.

Original language	English
Title of host publication	SoCPaR 2009 - Soft Computing and Pattern Recognition
Pages	545-550
Number of pages	6
DOIs	https://doi.org/10.1109/SoCPaR.2009.109
Publication status	Published - 2009 Dec 1
Event	International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009 - Malacca, Malaysia Duration: 2009 Dec 4 → 2009 Dec 7

Publication series

Name	SoCPaR 2009 - Soft Computing and Pattern Recognition

Other

Other	International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009
Country/Territory	Malaysia
City	Malacca
Period	09/12/4 → 09/12/7

Keywords

Artificial neural networks
Digital rights management
Digital watermarking
Information hiding
Machine learning

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Vision and Pattern Recognition
Software

Access to Document

10.1109/SoCPaR.2009.109

Cite this

Damageless digital watermarking by machine learning : A method of key generation for information extraction using artificial neural networks. / Naoe, Kensuke; Sasaki, Hideyasu; Takefuji, Yoshiyasu.

SoCPaR 2009 - Soft Computing and Pattern Recognition. 2009. p. 545-550 5368676 (SoCPaR 2009 - Soft Computing and Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Naoe, K, Sasaki, H & Takefuji, Y 2009, Damageless digital watermarking by machine learning: A method of key generation for information extraction using artificial neural networks. in SoCPaR 2009 - Soft Computing and Pattern Recognition., 5368676, SoCPaR 2009 - Soft Computing and Pattern Recognition, pp. 545-550, International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009, Malacca, Malaysia, 09/12/4. https://doi.org/10.1109/SoCPaR.2009.109

@inproceedings{64d165524f5a4afdb27486dc24337798,

title = "Damageless digital watermarking by machine learning: A method of key generation for information extraction using artificial neural networks",

abstract = "Soft computing in the area of information security is a promising field for the creation of intelligent solutions. This paper discusses a method for digital watermarking using artificial neural networks to realize secure copyright protection of visual information without any damage. The discussed watermark extraction keys and feature extraction keys identify the secure and unique hidden patterns for proper digital watermarks. In the experiments, we have shown that the proposed method is robust to high pass filtering and JPEG compression of visual information, only for those watermark extraction keys which were able to identify the proper hidden bit patterns from original visual information using corresponding feature extraction keys. The proposed method is to contribute to secure visual digital watermarking without damaging or losing any detailed data of visual information.",

keywords = "Artificial neural networks, Digital rights management, Digital watermarking, Information hiding, Machine learning",

author = "Kensuke Naoe and Hideyasu Sasaki and Yoshiyasu Takefuji",

year = "2009",

month = dec,

day = "1",

doi = "10.1109/SoCPaR.2009.109",

language = "English",

isbn = "9780769538792",

series = "SoCPaR 2009 - Soft Computing and Pattern Recognition",

pages = "545--550",

booktitle = "SoCPaR 2009 - Soft Computing and Pattern Recognition",

note = "International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009 ; Conference date: 04-12-2009 Through 07-12-2009",

}

TY - GEN

T1 - Damageless digital watermarking by machine learning

T2 - International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009

AU - Naoe, Kensuke

AU - Sasaki, Hideyasu

AU - Takefuji, Yoshiyasu

PY - 2009/12/1

Y1 - 2009/12/1

N2 - Soft computing in the area of information security is a promising field for the creation of intelligent solutions. This paper discusses a method for digital watermarking using artificial neural networks to realize secure copyright protection of visual information without any damage. The discussed watermark extraction keys and feature extraction keys identify the secure and unique hidden patterns for proper digital watermarks. In the experiments, we have shown that the proposed method is robust to high pass filtering and JPEG compression of visual information, only for those watermark extraction keys which were able to identify the proper hidden bit patterns from original visual information using corresponding feature extraction keys. The proposed method is to contribute to secure visual digital watermarking without damaging or losing any detailed data of visual information.

AB - Soft computing in the area of information security is a promising field for the creation of intelligent solutions. This paper discusses a method for digital watermarking using artificial neural networks to realize secure copyright protection of visual information without any damage. The discussed watermark extraction keys and feature extraction keys identify the secure and unique hidden patterns for proper digital watermarks. In the experiments, we have shown that the proposed method is robust to high pass filtering and JPEG compression of visual information, only for those watermark extraction keys which were able to identify the proper hidden bit patterns from original visual information using corresponding feature extraction keys. The proposed method is to contribute to secure visual digital watermarking without damaging or losing any detailed data of visual information.

KW - Artificial neural networks

KW - Digital rights management

KW - Digital watermarking

KW - Information hiding

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=77649324871&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77649324871&partnerID=8YFLogxK

U2 - 10.1109/SoCPaR.2009.109

DO - 10.1109/SoCPaR.2009.109

M3 - Conference contribution

AN - SCOPUS:77649324871

SN - 9780769538792

T3 - SoCPaR 2009 - Soft Computing and Pattern Recognition

SP - 545

EP - 550

BT - SoCPaR 2009 - Soft Computing and Pattern Recognition

Y2 - 4 December 2009 through 7 December 2009

ER -

Damageless digital watermarking by machine learning: A method of key generation for information extraction using artificial neural networks

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this