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基于三角极性特征分类的视频伪造检测模型

IF 0.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
International Journal of Digital Crime and Forensics Pub Date : 2020-01-01 DOI:10.4018/ijdcf.2020010102
Chee Cheun Huang, Chien Eao Lee, V. Thing
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引用次数: 5

摘要

Videoforgeryhasbeenincreasingovertheyearsduetothewideaccessibilityofsophisticatedvideo editingsoftware。Ahighlyaccurateandautomatedvideoforgerydetectionsystemwillthereforebe vitallyimportantinensuringtheauthenticityofforensicvideoevidences。Thisarticleproposesanovel TriangularPolarityFeatureClassification(TPFC)videoforgerydetectionframeworkforvideoframe insertionanddeletionforgeries。TheTPFCframeworkhashighprecisionandrecallrateswithasimple andthreshold-lessalgorithmdesignedforreal-worldapplications。Systemrobustnessevaluationsbased oncrossvalidationanddifferentdatabaserecordingconditionswerealsoperformedandvalidated。EvaluationontheperformanceoftheTPFCframeworkdemonstratedtheefficacyoftheproposed frameworkbyachievingarecallrateofupto98.26%andprecisionrateofupto95.76%，aswellas highlocalizationaccuracyondetectedforgedvideos。TheTPFCframeworkisfurtherdemonstrated tobecapableofoutperformingothermodernvideoforgerydetectiontechniquesavailabletoday。关键词:帧删除，帧插入，帧间伪造检测，准确率，召回率，视频取证
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A Novel Video Forgery Detection Model Based on Triangular Polarity Feature Classification
Videoforgeryhasbeenincreasingovertheyearsduetothewideaccessibilityofsophisticatedvideo editingsoftware.Ahighlyaccurateandautomatedvideoforgerydetectionsystemwillthereforebe vitallyimportantinensuringtheauthenticityofforensicvideoevidences.Thisarticleproposesanovel TriangularPolarityFeatureClassification(TPFC)videoforgerydetectionframeworkforvideoframe insertionanddeletionforgeries.TheTPFCframeworkhashighprecisionandrecallrateswithasimple andthreshold-lessalgorithmdesignedforreal-worldapplications.Systemrobustnessevaluationsbased oncrossvalidationanddifferentdatabaserecordingconditionswerealsoperformedandvalidated. EvaluationontheperformanceoftheTPFCframeworkdemonstratedtheefficacyoftheproposed frameworkbyachievingarecallrateofupto98.26%andprecisionrateofupto95.76%,aswellas highlocalizationaccuracyondetectedforgedvideos.TheTPFCframeworkisfurtherdemonstrated tobecapableofoutperformingothermodernvideoforgerydetectiontechniquesavailabletoday. KeywoRDS Frame Deletion, Frame Insertion, Inter-frame Forgery Detection, Precision Rate, Recall Rate, Video Forensic
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来源期刊
International Journal of Digital Crime and Forensics
International Journal of Digital Crime and Forensics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
2.70
自引率
0.00%
发文量
15
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