Chroma key foreground forgery detection under various attacks in digital video based on frame edge identification
Abstract Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techni...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Singh, Gurvinder [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 81(2021), 1 vom: 02. Okt., Seite 1419-1446 |
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| Übergeordnetes Werk: |
volume:81 ; year:2021 ; number:1 ; day:02 ; month:10 ; pages:1419-1446 |
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| DOI / URN: |
10.1007/s11042-021-11380-3 |
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SPR04601506X |
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| 520 | |a Abstract Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques. | ||
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10.1007/s11042-021-11380-3 doi (DE-627)SPR04601506X (SPR)s11042-021-11380-3-e DE-627 ger DE-627 rakwb eng Singh, Gurvinder verfasserin aut Chroma key foreground forgery detection under various attacks in digital video based on frame edge identification 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques. Chroma key foreground forgery (dpeaa)DE-He213 Difference frame (dpeaa)DE-He213 Edge frame (dpeaa)DE-He213 Green/Blue screen composition (dpeaa)DE-He213 Attacks (dpeaa)DE-He213 Singh, Kulbir (orcid)0000-0001-8070-3395 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2021), 1 vom: 02. Okt., Seite 1419-1446 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2021 number:1 day:02 month:10 pages:1419-1446 https://dx.doi.org/10.1007/s11042-021-11380-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2021 1 02 10 1419-1446 |
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10.1007/s11042-021-11380-3 doi (DE-627)SPR04601506X (SPR)s11042-021-11380-3-e DE-627 ger DE-627 rakwb eng Singh, Gurvinder verfasserin aut Chroma key foreground forgery detection under various attacks in digital video based on frame edge identification 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques. Chroma key foreground forgery (dpeaa)DE-He213 Difference frame (dpeaa)DE-He213 Edge frame (dpeaa)DE-He213 Green/Blue screen composition (dpeaa)DE-He213 Attacks (dpeaa)DE-He213 Singh, Kulbir (orcid)0000-0001-8070-3395 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2021), 1 vom: 02. Okt., Seite 1419-1446 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2021 number:1 day:02 month:10 pages:1419-1446 https://dx.doi.org/10.1007/s11042-021-11380-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2021 1 02 10 1419-1446 |
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10.1007/s11042-021-11380-3 doi (DE-627)SPR04601506X (SPR)s11042-021-11380-3-e DE-627 ger DE-627 rakwb eng Singh, Gurvinder verfasserin aut Chroma key foreground forgery detection under various attacks in digital video based on frame edge identification 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques. Chroma key foreground forgery (dpeaa)DE-He213 Difference frame (dpeaa)DE-He213 Edge frame (dpeaa)DE-He213 Green/Blue screen composition (dpeaa)DE-He213 Attacks (dpeaa)DE-He213 Singh, Kulbir (orcid)0000-0001-8070-3395 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2021), 1 vom: 02. Okt., Seite 1419-1446 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2021 number:1 day:02 month:10 pages:1419-1446 https://dx.doi.org/10.1007/s11042-021-11380-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2021 1 02 10 1419-1446 |
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10.1007/s11042-021-11380-3 doi (DE-627)SPR04601506X (SPR)s11042-021-11380-3-e DE-627 ger DE-627 rakwb eng Singh, Gurvinder verfasserin aut Chroma key foreground forgery detection under various attacks in digital video based on frame edge identification 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques. Chroma key foreground forgery (dpeaa)DE-He213 Difference frame (dpeaa)DE-He213 Edge frame (dpeaa)DE-He213 Green/Blue screen composition (dpeaa)DE-He213 Attacks (dpeaa)DE-He213 Singh, Kulbir (orcid)0000-0001-8070-3395 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2021), 1 vom: 02. Okt., Seite 1419-1446 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2021 number:1 day:02 month:10 pages:1419-1446 https://dx.doi.org/10.1007/s11042-021-11380-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2021 1 02 10 1419-1446 |
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10.1007/s11042-021-11380-3 doi (DE-627)SPR04601506X (SPR)s11042-021-11380-3-e DE-627 ger DE-627 rakwb eng Singh, Gurvinder verfasserin aut Chroma key foreground forgery detection under various attacks in digital video based on frame edge identification 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques. Chroma key foreground forgery (dpeaa)DE-He213 Difference frame (dpeaa)DE-He213 Edge frame (dpeaa)DE-He213 Green/Blue screen composition (dpeaa)DE-He213 Attacks (dpeaa)DE-He213 Singh, Kulbir (orcid)0000-0001-8070-3395 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2021), 1 vom: 02. Okt., Seite 1419-1446 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2021 number:1 day:02 month:10 pages:1419-1446 https://dx.doi.org/10.1007/s11042-021-11380-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2021 1 02 10 1419-1446 |
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This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. 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Singh, Gurvinder |
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chroma key foreground forgery detection under various attacks in digital video based on frame edge identification |
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Chroma key foreground forgery detection under various attacks in digital video based on frame edge identification |
| abstract |
Abstract Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Chroma key foreground forgery is the most common forgery to manipulate the contents of a digital video. This kind of video can be presented in courts to show fake evidence, in political campaigns to misguide people and in the social media to distribute fake information. There are few techniques to discover this type of forgery. But a technique is required to detect the chroma key foreground forgery in the digital videos under various attacks to provide its robustness. In this paper, a passive approach is proposed for the detection of chroma key foreground forgery which is based on frame edge identification. The proposed approach generates difference frames for each frame pair of a digital video. The edges of each difference frame are identified in the proposed approach using canny edge detector. Then the proposed approach differentiates the edges pixels of each edge frame into large and small edge pixel difference values by applying threshold. After detection of the chroma key foreground forgery, the forged foreground is isolated from the authentic part of each edge frame in the digital video. Then the isolated forged foreground is localized and tracked within each frame of a forged digital video. The proposed approach is examined on the digital videos with different cases which shows high recall rate than precision rate in the experimental work. The proposed approach is then evaluated on the digital videos which are forged under the various attacks for its effective robustness. The proposed approach is also performed effectively on digital videos having realistic cases of chroma key foreground forgery with efficient results. The experimental results indicate higher detection accuracy, lower execution time and better robustness of the proposed approach than the other existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Chroma key foreground forgery detection under various attacks in digital video based on frame edge identification |
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https://dx.doi.org/10.1007/s11042-021-11380-3 |
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Singh, Kulbir |
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Singh, Kulbir |
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10.1007/s11042-021-11380-3 |
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| score |
7.398122 |