{"data":{"id":"10.6084/m9.figshare.5160982.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.5160982.v1","prefix":"10.6084","suffix":"m9.figshare.5160982.v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Sun, Jee-Young","nameType":"Personal","givenName":"Jee-Young","familyName":"Sun","affiliation":[],"nameIdentifiers":[]},{"name":"Seung-Wook Kim","affiliation":[],"nameIdentifiers":[]},{"name":"Sung-Jea Ko","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Contrast Enhancement Forensics based on Convolutional Neural Networks(CNN)"}],"publisher":"figshare","container":{},"publicationYear":2017,"subjects":[{"subject":"90699 Electrical and Electronic Engineering not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2017-06-30","dateType":"Created"},{"date":"2017-06-30","dateType":"Updated"},{"date":"2017","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsIdenticalTo","relatedIdentifier":"10.6084/m9.figshare.5160982","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["5527817153 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This work is about a CNN-based contrast enhancement image forensic method, which can verify autheticity of digital images, i.e., it can distinguish the contrast enhancement forgeries from unaltered images. \u003cbr\u003eThe dataset consists of grey level co-occurrence matrix(GLCM) of three types of images: unaltered images, contrast-enhanced images processed by common contrast enhancement techniques (histogram stretching, gamma correction, and S-curve mapping), and the images manipulated by using three current counter-forensic attacks. \u003cbr\u003e\u003cbr\u003eREADME.pdf has descriptions of the other .tar.gz format files. \u003cbr\u003e\u003cbr\u003e- Train dataset.tar.gz: train data\u003cbr\u003e- Test dataset.tar.gz: test data\u003cbr\u003e- CEForensicsCNN.tar.gz: source codes \u0026amp; trained model\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://figshare.com/articles/dataset/Contrast_Enhancement_Forensics_based_on_Convolutional_Neural_Networks_CNN_/5160982/1","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":1,"viewsOverTime":[{"yearMonth":"2025-10","total":1}],"downloadCount":0,"downloadsOverTime":[{"yearMonth":"2025-10","total":0}],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2017-06-30T09:06:18.000Z","registered":"2017-06-30T09:06:18.000Z","published":"2017","updated":"2020-09-02T06:47:51.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","type":"providers"}},"media":{"data":{"id":"10.6084/m9.figshare.5160982.v1","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}