A critical literature survey and prospects on tampering and anomaly detection in image data

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dc.contributor.authorda Costa, Kelton A.P.
dc.contributor.authorPapa, João P.
dc.contributor.authorPassos, Leandro A.
dc.contributor.authorColombo, Danilo
dc.contributor.authorSer, Javier Del
dc.contributor.authorMuhammad, Khan
dc.contributor.authorde Albuquerque, Victor Hugo C.
dc.contributor.institutionIA
dc.date.accessioned2024-09-10T14:50:03Z
dc.date.available2024-09-10T14:50:03Z
dc.date.issued2020-12
dc.descriptionPublisher Copyright: © 2020 Elsevier B.V.
dc.description.abstractConcernings related to image security have increased in the last years. One of the main reasons relies on the replacement of conventional photography to digital images, once the development of new technologies for image processing, as much as it has helped in the evolution of many new techniques in forensic studies, it also provided tools for image tampering. In this context, many companies and researchers devoted many efforts towards methods for detecting such tampered images, mostly aided by autonomous intelligent systems. Therefore, this work focuses on introducing a rigorous survey contemplating the state-of-the-art literature on computer-aided tampered image detection using machine learning techniques, as well as evolutionary computation, neural networks, fuzzy logic, Bayesian reasoning, among others. Besides, it also contemplates anomaly detection methods in the context of images due to the intrinsic relation between anomalies and tampering. Moreover, it aims at recent and in-depth researches relevant to the context of image tampering detection, performing a survey over more than 100 works related to the subject, spanning across different themes related to image tampering detection. Finally, a critical analysis is performed over this comprehensive compilation of literature, yielding some research opportunities and discussing some challenges in an attempt to align future efforts of the community with the niches and gaps remarked in this exciting field.en
dc.description.sponsorshipThe authors are grateful to Fundação de Amparo á Pesquisa do Estado de São Paulo (FAPESP), Brazil grants #2017/22905-6 , #2013/07375-0 , #2014/12236-1 , and #2016/19403-6 and by the Brazilian National Council for Research and Development (CNPq) via grants -8 , -6 , -1 , -7 and -6 , Petrobras, Brazil (grant -0 ) for their financial support and Basque Government, Spain for its funding support through the ELKARTEK and EMAITEK funding programs, support from the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1294-19 ).
dc.description.statusPeer reviewed
dc.identifier.citationda Costa , K A P , Papa , J P , Passos , L A , Colombo , D , Ser , J D , Muhammad , K & de Albuquerque , V H C 2020 , ' A critical literature survey and prospects on tampering and anomaly detection in image data ' , Applied Soft Computing Journal , vol. 97 , 106727 . https://doi.org/10.1016/j.asoc.2020.106727
dc.identifier.doi10.1016/j.asoc.2020.106727
dc.identifier.issn1568-4946
dc.identifier.urihttps://hdl.handle.net/11556/5198
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85091712536&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofApplied Soft Computing Journal
dc.relation.projectIDDepartment of Education of the Basque Government, IT1294-19
dc.relation.projectIDFundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP, 2014/12236-1-2017/22905-6-2016/19403-6-2013/07375-0
dc.relation.projectIDEusko Jaurlaritza
dc.relation.projectIDConselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, -6--7--8--1
dc.relation.projectIDPetrobras, -0
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsImage color analysis
dc.subject.keywordsImage forgery detection
dc.subject.keywordsImage splicing detection
dc.subject.keywordsImage tampering detection
dc.subject.keywordsNoise
dc.subject.keywordsSoftware
dc.titleA critical literature survey and prospects on tampering and anomaly detection in image dataen
dc.typejournal article
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