Support Vector Machines (SVM)-based biometric watermarking for offline handwritten signature
Biometric watermarking refers to the incorporation of biometrics and watermarking technology. In this paper, we present a novel biometric watermarking scheme to embed handwritten signature in the host as a notice of legitimate ownership. The core of the proposed method is the synergistic integration...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2008
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2827/ |
| _version_ | 1848790160299261952 |
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| author | Cheng-Yaw, Low Andrew Beng-Jin, Teoh Connie, Tee |
| author_facet | Cheng-Yaw, Low Andrew Beng-Jin, Teoh Connie, Tee |
| author_sort | Cheng-Yaw, Low |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | Biometric watermarking refers to the incorporation of biometrics and watermarking technology. In this paper, we present a novel biometric watermarking scheme to embed handwritten signature in the host as a notice of legitimate ownership. The core of the proposed method is the synergistic integration of a statistical classifier, i.e. the Support Vector Machine, with biometric watermarking to precisely extract the signature code from the host. We abbreviate the proposed method as SVM-BW. The performance of SVM-BW is validated against simulated frequency and geometric attacks, which include JPG compression, low pass filtering, median filtering, noise addition, scaling, rotation and cropping. Experiment results reveal that SVM-BW is able to endure severe degradation on the host fidelity. Furthermore, SVM-BW shows remarkable robustness even if the host is deliberately distorted. |
| first_indexed | 2025-11-14T18:08:12Z |
| format | Conference or Workshop Item |
| id | mmu-2827 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:08:12Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-28272011-09-19T07:54:28Z http://shdl.mmu.edu.my/2827/ Support Vector Machines (SVM)-based biometric watermarking for offline handwritten signature Cheng-Yaw, Low Andrew Beng-Jin, Teoh Connie, Tee T Technology (General) QA75.5-76.95 Electronic computers. Computer science Biometric watermarking refers to the incorporation of biometrics and watermarking technology. In this paper, we present a novel biometric watermarking scheme to embed handwritten signature in the host as a notice of legitimate ownership. The core of the proposed method is the synergistic integration of a statistical classifier, i.e. the Support Vector Machine, with biometric watermarking to precisely extract the signature code from the host. We abbreviate the proposed method as SVM-BW. The performance of SVM-BW is validated against simulated frequency and geometric attacks, which include JPG compression, low pass filtering, median filtering, noise addition, scaling, rotation and cropping. Experiment results reveal that SVM-BW is able to endure severe degradation on the host fidelity. Furthermore, SVM-BW shows remarkable robustness even if the host is deliberately distorted. 2008-06 Conference or Workshop Item NonPeerReviewed Cheng-Yaw, Low and Andrew Beng-Jin, Teoh and Connie, Tee (2008) Support Vector Machines (SVM)-based biometric watermarking for offline handwritten signature. In: 3rd IEEE Conference on Industrial Electronics and Applications (ICIEA 2008) , 03-05 June 2008, Singapore, SINGAPORE. http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=1&SID=N2eNo9mH22aE1IjOpib&page=87&doc=862 |
| spellingShingle | T Technology (General) QA75.5-76.95 Electronic computers. Computer science Cheng-Yaw, Low Andrew Beng-Jin, Teoh Connie, Tee Support Vector Machines (SVM)-based biometric watermarking for offline handwritten signature |
| title | Support Vector Machines (SVM)-based biometric watermarking for offline handwritten signature |
| title_full | Support Vector Machines (SVM)-based biometric watermarking for offline handwritten signature |
| title_fullStr | Support Vector Machines (SVM)-based biometric watermarking for offline handwritten signature |
| title_full_unstemmed | Support Vector Machines (SVM)-based biometric watermarking for offline handwritten signature |
| title_short | Support Vector Machines (SVM)-based biometric watermarking for offline handwritten signature |
| title_sort | support vector machines (svm)-based biometric watermarking for offline handwritten signature |
| topic | T Technology (General) QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2827/ http://shdl.mmu.edu.my/2827/ |