Image-format-independent tampered image detection based on overlapping concurrent directional patterns and neural networks
Abstract With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG fo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Meng-Luen [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 47(2017), 2 vom: 13. März, Seite 347-361 |
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| Übergeordnetes Werk: |
volume:47 ; year:2017 ; number:2 ; day:13 ; month:03 ; pages:347-361 |
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| DOI / URN: |
10.1007/s10489-017-0893-4 |
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| 520 | |a Abstract With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG format, the proposed method is image format independent, implying that it works with both noncompressed images and images in all compression formats. To improve the authentication accuracy, some strategies, such as overlapping image blocks only on concurrent directions, using a two-scale local binary pattern operator, and choosing the mean deviation instead of the standard deviation, are applied. A back-propagation neural network (BPNN) is used instead of support vector machines (SVMs) for classification to make online learning easier and achieve higher accuracy. In our experiments, we used the CASIA Database (CASIA TIDE v1.0) of compressed images and the Columbia University Digital Video Multimedia (DVMM) dataset of uncompressed images to evaluate our image authentication method. This benchmark dataset includes two types of image tampering, namely image splicing and copy–move forgery. Experiments were performed using both the SVM and BPNN classifiers with various parameters. We determined that the BPNN achieved a higher accuracy of up to 97.26 %. | ||
| 650 | 4 | |a Digital image forensics | |
| 650 | 4 | |a Digital image authentication | |
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| 700 | 1 | |a Chen, Yi-Fan |4 aut | |
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10.1007/s10489-017-0893-4 doi (DE-627)OLC2066102997 (DE-He213)s10489-017-0893-4-p DE-627 ger DE-627 rakwb eng 004 VZ Wu, Meng-Luen verfasserin (orcid)0000-0002-8017-8073 aut Image-format-independent tampered image detection based on overlapping concurrent directional patterns and neural networks 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG format, the proposed method is image format independent, implying that it works with both noncompressed images and images in all compression formats. To improve the authentication accuracy, some strategies, such as overlapping image blocks only on concurrent directions, using a two-scale local binary pattern operator, and choosing the mean deviation instead of the standard deviation, are applied. A back-propagation neural network (BPNN) is used instead of support vector machines (SVMs) for classification to make online learning easier and achieve higher accuracy. In our experiments, we used the CASIA Database (CASIA TIDE v1.0) of compressed images and the Columbia University Digital Video Multimedia (DVMM) dataset of uncompressed images to evaluate our image authentication method. This benchmark dataset includes two types of image tampering, namely image splicing and copy–move forgery. Experiments were performed using both the SVM and BPNN classifiers with various parameters. We determined that the BPNN achieved a higher accuracy of up to 97.26 %. Digital image forensics Digital image authentication Tampered image detection Artificial neural network Fahn, Chin-Shyurng aut Chen, Yi-Fan aut Enthalten in Applied intelligence Springer US, 1991 47(2017), 2 vom: 13. März, Seite 347-361 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:47 year:2017 number:2 day:13 month:03 pages:347-361 https://doi.org/10.1007/s10489-017-0893-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 47 2017 2 13 03 347-361 |
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10.1007/s10489-017-0893-4 doi (DE-627)OLC2066102997 (DE-He213)s10489-017-0893-4-p DE-627 ger DE-627 rakwb eng 004 VZ Wu, Meng-Luen verfasserin (orcid)0000-0002-8017-8073 aut Image-format-independent tampered image detection based on overlapping concurrent directional patterns and neural networks 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG format, the proposed method is image format independent, implying that it works with both noncompressed images and images in all compression formats. To improve the authentication accuracy, some strategies, such as overlapping image blocks only on concurrent directions, using a two-scale local binary pattern operator, and choosing the mean deviation instead of the standard deviation, are applied. A back-propagation neural network (BPNN) is used instead of support vector machines (SVMs) for classification to make online learning easier and achieve higher accuracy. In our experiments, we used the CASIA Database (CASIA TIDE v1.0) of compressed images and the Columbia University Digital Video Multimedia (DVMM) dataset of uncompressed images to evaluate our image authentication method. This benchmark dataset includes two types of image tampering, namely image splicing and copy–move forgery. Experiments were performed using both the SVM and BPNN classifiers with various parameters. We determined that the BPNN achieved a higher accuracy of up to 97.26 %. Digital image forensics Digital image authentication Tampered image detection Artificial neural network Fahn, Chin-Shyurng aut Chen, Yi-Fan aut Enthalten in Applied intelligence Springer US, 1991 47(2017), 2 vom: 13. März, Seite 347-361 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:47 year:2017 number:2 day:13 month:03 pages:347-361 https://doi.org/10.1007/s10489-017-0893-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 47 2017 2 13 03 347-361 |
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10.1007/s10489-017-0893-4 doi (DE-627)OLC2066102997 (DE-He213)s10489-017-0893-4-p DE-627 ger DE-627 rakwb eng 004 VZ Wu, Meng-Luen verfasserin (orcid)0000-0002-8017-8073 aut Image-format-independent tampered image detection based on overlapping concurrent directional patterns and neural networks 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG format, the proposed method is image format independent, implying that it works with both noncompressed images and images in all compression formats. To improve the authentication accuracy, some strategies, such as overlapping image blocks only on concurrent directions, using a two-scale local binary pattern operator, and choosing the mean deviation instead of the standard deviation, are applied. A back-propagation neural network (BPNN) is used instead of support vector machines (SVMs) for classification to make online learning easier and achieve higher accuracy. In our experiments, we used the CASIA Database (CASIA TIDE v1.0) of compressed images and the Columbia University Digital Video Multimedia (DVMM) dataset of uncompressed images to evaluate our image authentication method. This benchmark dataset includes two types of image tampering, namely image splicing and copy–move forgery. Experiments were performed using both the SVM and BPNN classifiers with various parameters. We determined that the BPNN achieved a higher accuracy of up to 97.26 %. Digital image forensics Digital image authentication Tampered image detection Artificial neural network Fahn, Chin-Shyurng aut Chen, Yi-Fan aut Enthalten in Applied intelligence Springer US, 1991 47(2017), 2 vom: 13. März, Seite 347-361 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:47 year:2017 number:2 day:13 month:03 pages:347-361 https://doi.org/10.1007/s10489-017-0893-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 47 2017 2 13 03 347-361 |
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10.1007/s10489-017-0893-4 doi (DE-627)OLC2066102997 (DE-He213)s10489-017-0893-4-p DE-627 ger DE-627 rakwb eng 004 VZ Wu, Meng-Luen verfasserin (orcid)0000-0002-8017-8073 aut Image-format-independent tampered image detection based on overlapping concurrent directional patterns and neural networks 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG format, the proposed method is image format independent, implying that it works with both noncompressed images and images in all compression formats. To improve the authentication accuracy, some strategies, such as overlapping image blocks only on concurrent directions, using a two-scale local binary pattern operator, and choosing the mean deviation instead of the standard deviation, are applied. A back-propagation neural network (BPNN) is used instead of support vector machines (SVMs) for classification to make online learning easier and achieve higher accuracy. In our experiments, we used the CASIA Database (CASIA TIDE v1.0) of compressed images and the Columbia University Digital Video Multimedia (DVMM) dataset of uncompressed images to evaluate our image authentication method. This benchmark dataset includes two types of image tampering, namely image splicing and copy–move forgery. Experiments were performed using both the SVM and BPNN classifiers with various parameters. We determined that the BPNN achieved a higher accuracy of up to 97.26 %. Digital image forensics Digital image authentication Tampered image detection Artificial neural network Fahn, Chin-Shyurng aut Chen, Yi-Fan aut Enthalten in Applied intelligence Springer US, 1991 47(2017), 2 vom: 13. März, Seite 347-361 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:47 year:2017 number:2 day:13 month:03 pages:347-361 https://doi.org/10.1007/s10489-017-0893-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 47 2017 2 13 03 347-361 |
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Abstract With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG format, the proposed method is image format independent, implying that it works with both noncompressed images and images in all compression formats. To improve the authentication accuracy, some strategies, such as overlapping image blocks only on concurrent directions, using a two-scale local binary pattern operator, and choosing the mean deviation instead of the standard deviation, are applied. A back-propagation neural network (BPNN) is used instead of support vector machines (SVMs) for classification to make online learning easier and achieve higher accuracy. In our experiments, we used the CASIA Database (CASIA TIDE v1.0) of compressed images and the Columbia University Digital Video Multimedia (DVMM) dataset of uncompressed images to evaluate our image authentication method. This benchmark dataset includes two types of image tampering, namely image splicing and copy–move forgery. Experiments were performed using both the SVM and BPNN classifiers with various parameters. We determined that the BPNN achieved a higher accuracy of up to 97.26 %. © Springer Science+Business Media New York 2017 |
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Abstract With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG format, the proposed method is image format independent, implying that it works with both noncompressed images and images in all compression formats. To improve the authentication accuracy, some strategies, such as overlapping image blocks only on concurrent directions, using a two-scale local binary pattern operator, and choosing the mean deviation instead of the standard deviation, are applied. A back-propagation neural network (BPNN) is used instead of support vector machines (SVMs) for classification to make online learning easier and achieve higher accuracy. In our experiments, we used the CASIA Database (CASIA TIDE v1.0) of compressed images and the Columbia University Digital Video Multimedia (DVMM) dataset of uncompressed images to evaluate our image authentication method. This benchmark dataset includes two types of image tampering, namely image splicing and copy–move forgery. Experiments were performed using both the SVM and BPNN classifiers with various parameters. We determined that the BPNN achieved a higher accuracy of up to 97.26 %. © Springer Science+Business Media New York 2017 |
| abstract_unstemmed |
Abstract With the advancement of photo editing software, digital documents can easily be altered, which causes some legal issues. This paper proposes an image authentication method, which determines whether an image is authentic. Unlike many existing methods that only work with images in the JPEG format, the proposed method is image format independent, implying that it works with both noncompressed images and images in all compression formats. To improve the authentication accuracy, some strategies, such as overlapping image blocks only on concurrent directions, using a two-scale local binary pattern operator, and choosing the mean deviation instead of the standard deviation, are applied. A back-propagation neural network (BPNN) is used instead of support vector machines (SVMs) for classification to make online learning easier and achieve higher accuracy. In our experiments, we used the CASIA Database (CASIA TIDE v1.0) of compressed images and the Columbia University Digital Video Multimedia (DVMM) dataset of uncompressed images to evaluate our image authentication method. This benchmark dataset includes two types of image tampering, namely image splicing and copy–move forgery. Experiments were performed using both the SVM and BPNN classifiers with various parameters. We determined that the BPNN achieved a higher accuracy of up to 97.26 %. © Springer Science+Business Media New York 2017 |
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Image-format-independent tampered image detection based on overlapping concurrent directional patterns and neural networks |
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Fahn, Chin-Shyurng Chen, Yi-Fan |
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