{"title":"基于CFA伪影的深度学习数字图像拼接检测","authors":"Nadheer Younus Hussien, R. Mahmoud, Hala H. Zayed","doi":"10.4018/ijskd.2020040102","DOIUrl":null,"url":null,"abstract":"Digitalimageforgeryisaseriousproblemofanincreasingattentionfromtheresearchsociety.Image splicingisawell-knowntypeofdigitalimageforgeryinwhichtheforgedimageissynthesizedfrom twoormoreimages.Splicingforgerydetectionismorechallengingwhencomparedwithotherforgery typesbecausetheforgedimagedoesnotcontainanyduplicatedregions.Inaddition,unavailabilityof sourceimagesintroducesnoevidenceabouttheforgeryprocess.Inthisstudy,anautomatedimage splicingforgerydetectionschemeispresented.Itdependsonextractingthefeatureofimagesbased ontheanalysisofcolorfilterarray(CFA).Afeaturereductionprocessisperformedusingprincipal componentanalysis (PCA) to reduce thedimensionalityof the resulting featurevectors.Adeep beliefnetwork-basedclassifierisbuiltandtrainedtoclassifythetestedimagesasauthenticorspliced images.TheproposedschemeisevaluatedthroughasetofexperimentsonColumbiaImageSplicing DetectionEvaluationDataset(CISDED)underdifferentscenariosincludingaddingpostprocessing onthesplicedimagessuchJPEGcompressionandGaussianNoise.Theobtainedresultsrevealthat theproposedschemeexhibitsapromisingperformancewith95.05%precision,94.05%recall,94.05% truepositiverate,and98.197%accuracy.Moreover,theobtainedresultsshowthesuperiorityofthe proposedschemecomparedtootherrecentsplicingdetectionmethod. KeywoRDS Color Filter Array, Deep Belief Network, Deep Learning, Digital Image Forgery, Splicing Forgery","PeriodicalId":13656,"journal":{"name":"Int. J. Sociotechnology Knowl. Dev.","volume":"98 1","pages":"31-44"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Deep Learning on Digital Image Splicing Detection Using CFA Artifacts\",\"authors\":\"Nadheer Younus Hussien, R. Mahmoud, Hala H. Zayed\",\"doi\":\"10.4018/ijskd.2020040102\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Digitalimageforgeryisaseriousproblemofanincreasingattentionfromtheresearchsociety.Image splicingisawell-knowntypeofdigitalimageforgeryinwhichtheforgedimageissynthesizedfrom twoormoreimages.Splicingforgerydetectionismorechallengingwhencomparedwithotherforgery typesbecausetheforgedimagedoesnotcontainanyduplicatedregions.Inaddition,unavailabilityof sourceimagesintroducesnoevidenceabouttheforgeryprocess.Inthisstudy,anautomatedimage splicingforgerydetectionschemeispresented.Itdependsonextractingthefeatureofimagesbased ontheanalysisofcolorfilterarray(CFA).Afeaturereductionprocessisperformedusingprincipal componentanalysis (PCA) to reduce thedimensionalityof the resulting featurevectors.Adeep beliefnetwork-basedclassifierisbuiltandtrainedtoclassifythetestedimagesasauthenticorspliced images.TheproposedschemeisevaluatedthroughasetofexperimentsonColumbiaImageSplicing DetectionEvaluationDataset(CISDED)underdifferentscenariosincludingaddingpostprocessing onthesplicedimagessuchJPEGcompressionandGaussianNoise.Theobtainedresultsrevealthat theproposedschemeexhibitsapromisingperformancewith95.05%precision,94.05%recall,94.05% truepositiverate,and98.197%accuracy.Moreover,theobtainedresultsshowthesuperiorityofthe proposedschemecomparedtootherrecentsplicingdetectionmethod. KeywoRDS Color Filter Array, Deep Belief Network, Deep Learning, Digital Image Forgery, Splicing Forgery\",\"PeriodicalId\":13656,\"journal\":{\"name\":\"Int. J. Sociotechnology Knowl. Dev.\",\"volume\":\"98 1\",\"pages\":\"31-44\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Sociotechnology Knowl. Dev.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijskd.2020040102\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Sociotechnology Knowl. Dev.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijskd.2020040102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Deep Learning on Digital Image Splicing Detection Using CFA Artifacts
Digitalimageforgeryisaseriousproblemofanincreasingattentionfromtheresearchsociety.Image splicingisawell-knowntypeofdigitalimageforgeryinwhichtheforgedimageissynthesizedfrom twoormoreimages.Splicingforgerydetectionismorechallengingwhencomparedwithotherforgery typesbecausetheforgedimagedoesnotcontainanyduplicatedregions.Inaddition,unavailabilityof sourceimagesintroducesnoevidenceabouttheforgeryprocess.Inthisstudy,anautomatedimage splicingforgerydetectionschemeispresented.Itdependsonextractingthefeatureofimagesbased ontheanalysisofcolorfilterarray(CFA).Afeaturereductionprocessisperformedusingprincipal componentanalysis (PCA) to reduce thedimensionalityof the resulting featurevectors.Adeep beliefnetwork-basedclassifierisbuiltandtrainedtoclassifythetestedimagesasauthenticorspliced images.TheproposedschemeisevaluatedthroughasetofexperimentsonColumbiaImageSplicing DetectionEvaluationDataset(CISDED)underdifferentscenariosincludingaddingpostprocessing onthesplicedimagessuchJPEGcompressionandGaussianNoise.Theobtainedresultsrevealthat theproposedschemeexhibitsapromisingperformancewith95.05%precision,94.05%recall,94.05% truepositiverate,and98.197%accuracy.Moreover,theobtainedresultsshowthesuperiorityofthe proposedschemecomparedtootherrecentsplicingdetectionmethod. KeywoRDS Color Filter Array, Deep Belief Network, Deep Learning, Digital Image Forgery, Splicing Forgery
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