Watermark Detection and Extraction Using Independent Component Analysis Method
Abstract This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detect...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yu, Dan [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2002 |
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| Schlagwörter: |
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| Anmerkung: |
© Yu et al. 2002 |
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| Übergeordnetes Werk: |
Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2002(2002), 1 vom: 14. Jan. |
|---|---|
| Übergeordnetes Werk: |
volume:2002 ; year:2002 ; number:1 ; day:14 ; month:01 |
| Links: |
|---|
| DOI / URN: |
10.1155/S111086570200046X |
|---|
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SPR03197175X |
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| 520 | |a Abstract This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA) whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion. | ||
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10.1155/S111086570200046X doi (DE-627)SPR03197175X (SPR)S111086570200046X-e DE-627 ger DE-627 rakwb eng Yu, Dan verfasserin aut Watermark Detection and Extraction Using Independent Component Analysis Method 2002 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Yu et al. 2002 Abstract This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA) whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion. watermarking (dpeaa)DE-He213 dewatermarking (dpeaa)DE-He213 independent component analysis (ICA) (dpeaa)DE-He213 Sattar, Farook aut Ma, Kai-Kuang aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2002(2002), 1 vom: 14. Jan. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2002 year:2002 number:1 day:14 month:01 https://dx.doi.org/10.1155/S111086570200046X lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 AR 2002 2002 1 14 01 |
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10.1155/S111086570200046X doi (DE-627)SPR03197175X (SPR)S111086570200046X-e DE-627 ger DE-627 rakwb eng Yu, Dan verfasserin aut Watermark Detection and Extraction Using Independent Component Analysis Method 2002 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Yu et al. 2002 Abstract This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA) whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion. watermarking (dpeaa)DE-He213 dewatermarking (dpeaa)DE-He213 independent component analysis (ICA) (dpeaa)DE-He213 Sattar, Farook aut Ma, Kai-Kuang aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2002(2002), 1 vom: 14. Jan. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2002 year:2002 number:1 day:14 month:01 https://dx.doi.org/10.1155/S111086570200046X lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 AR 2002 2002 1 14 01 |
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10.1155/S111086570200046X doi (DE-627)SPR03197175X (SPR)S111086570200046X-e DE-627 ger DE-627 rakwb eng Yu, Dan verfasserin aut Watermark Detection and Extraction Using Independent Component Analysis Method 2002 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Yu et al. 2002 Abstract This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA) whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion. watermarking (dpeaa)DE-He213 dewatermarking (dpeaa)DE-He213 independent component analysis (ICA) (dpeaa)DE-He213 Sattar, Farook aut Ma, Kai-Kuang aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2002(2002), 1 vom: 14. Jan. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2002 year:2002 number:1 day:14 month:01 https://dx.doi.org/10.1155/S111086570200046X lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 AR 2002 2002 1 14 01 |
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10.1155/S111086570200046X doi (DE-627)SPR03197175X (SPR)S111086570200046X-e DE-627 ger DE-627 rakwb eng Yu, Dan verfasserin aut Watermark Detection and Extraction Using Independent Component Analysis Method 2002 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Yu et al. 2002 Abstract This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA) whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion. watermarking (dpeaa)DE-He213 dewatermarking (dpeaa)DE-He213 independent component analysis (ICA) (dpeaa)DE-He213 Sattar, Farook aut Ma, Kai-Kuang aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2002(2002), 1 vom: 14. Jan. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2002 year:2002 number:1 day:14 month:01 https://dx.doi.org/10.1155/S111086570200046X lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 AR 2002 2002 1 14 01 |
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10.1155/S111086570200046X doi (DE-627)SPR03197175X (SPR)S111086570200046X-e DE-627 ger DE-627 rakwb eng Yu, Dan verfasserin aut Watermark Detection and Extraction Using Independent Component Analysis Method 2002 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Yu et al. 2002 Abstract This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA) whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion. watermarking (dpeaa)DE-He213 dewatermarking (dpeaa)DE-He213 independent component analysis (ICA) (dpeaa)DE-He213 Sattar, Farook aut Ma, Kai-Kuang aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2002(2002), 1 vom: 14. Jan. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2002 year:2002 number:1 day:14 month:01 https://dx.doi.org/10.1155/S111086570200046X lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 AR 2002 2002 1 14 01 |
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Watermark Detection and Extraction Using Independent Component Analysis Method |
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Watermark Detection and Extraction Using Independent Component Analysis Method |
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Yu, Dan |
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Elektronische Aufsätze |
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watermark detection and extraction using independent component analysis method |
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Watermark Detection and Extraction Using Independent Component Analysis Method |
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Abstract This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA) whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion. © Yu et al. 2002 |
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Abstract This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA) whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion. © Yu et al. 2002 |
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Abstract This paper proposes a new image watermarking technique, which adopts Independent Component Analysis (ICA) for watermark detection and extraction process (i.e., dewatermarking). Watermark embedding is performed in the spatial domain of the original image. Watermark can be successfully detected during the Principle Component Analysis (PCA) whitening stage. A nonlinear robust batch ICA algorithm, which is able to efficiently extract various temporally correlated sources from their observed linear mixtures, is used for blind watermark extraction. The evaluations illustrate the validity and good performance of the proposed watermark detection and extraction scheme based on ICA. The accuracy of watermark extraction depends on the statistical independence between the original, key and watermark images and the temporal correlation of these sources. Experimental results demonstrate that the proposed system is robust to several important image processing attacks, including some geometrical transformations—scaling, cropping and rotation, quantization, additive noise, low pass filtering, multiple marks, and collusion. © Yu et al. 2002 |
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