Deep Learning on Digital Image Splicing Detection Using CFA Artifacts

Int. J. Sociotechnology Knowl. Dev. Pub Date : 2020-04-01 DOI:10.4018/ijskd.2020040102

Nadheer Younus Hussien, R. Mahmoud, Hala H. Zayed

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引用次数: 12

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

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基于CFA伪影的深度学习数字图像拼接检测

Digitalimageforgeryisaseriousproblemofanincreasingattentionfromtheresearchsociety。Image splicingisawell-knowntypeofdigitalimageforgeryinwhichtheforgedimageissynthesizedfrom twoormoreimages。Splicingforgerydetectionismorechallengingwhencomparedwithotherforgery typesbecausetheforgedimagedoesnotcontainanyduplicatedregions。Inaddition，unavailabilityof sourceimagesintroducesnoevidenceabouttheforgeryprocess。Inthisstudy，anautomatedimage splicingforgerydetectionschemeispresented。Itdependsonextractingthefeatureofimagesbased ontheanalysisofcolorfilterarray(CFA)。Afeaturereductionprocessisperformedusingprincipal componentanalysis (PCA)→reduce→thedimensionalityof→结果→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。关键词:彩色滤波器阵列，深度信念网络，深度学习，数字图像伪造，拼接伪造

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Int. J. Sociotechnology Knowl. Dev.

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