Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks

Cited 18 time in webofscience Cited 0 time in scopus
  • Hit : 389
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, Dongkyuko
dc.contributor.authorJang, Han-Ulko
dc.contributor.authorMun, Seung-Minko
dc.contributor.authorChoi, Sung-Heeko
dc.contributor.authorLee, Heung-Kyuko
dc.date.accessioned2018-02-21T05:33:58Z-
dc.date.available2018-02-21T05:33:58Z-
dc.date.created2018-01-15-
dc.date.created2018-01-15-
dc.date.issued2018-02-
dc.identifier.citationIEEE SIGNAL PROCESSING LETTERS, v.25, no.2, pp.278 - 282-
dc.identifier.issn1070-9908-
dc.identifier.urihttp://hdl.handle.net/10203/240090-
dc.description.abstractMedian filtering is used as an anti-forensic technique to erase processing history of some image manipulations such as JPEG, resampling, etc. Thus, various detectors have been proposed to detect median filtered images. To counter these techniques, several anti-forensic methods have been devised as well. However, restoring the median filtered image is a typical ill-posed problem, and thus it is still difficult to reconstruct the image visually close to the original image. Also, it is further hard to make the restored image have the statistical characteristic of the raw image for the anti-forensic purpose. To solve this problem, we present a median filtering anti-forensic method based on deep convolutional neural networks, which can effectively remove traces from median filtered images. We adopt the framework of generative adversarial networks to generate images that follow the underlying statistics of unaltered images, significantly enhancing forensic undetectability. Through extensive experiments, we demonstrate that our method successfully deceives the existing median filtering forensic techniques.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.subjectDIGITAL IMAGES-
dc.subjectTRACES-
dc.titleMedian Filtered Image Restoration and Anti-Forensics Using Adversarial Networks-
dc.typeArticle-
dc.identifier.wosid000422752900001-
dc.identifier.scopusid2-s2.0-85038400662-
dc.type.rimsART-
dc.citation.volume25-
dc.citation.issue2-
dc.citation.beginningpage278-
dc.citation.endingpage282-
dc.citation.publicationnameIEEE SIGNAL PROCESSING LETTERS-
dc.identifier.doi10.1109/LSP.2017.2782363-
dc.contributor.localauthorChoi, Sung-Hee-
dc.contributor.localauthorLee, Heung-Kyu-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorAnti-forensics-
dc.subject.keywordAuthorconvolutional neural networks (CNNs)-
dc.subject.keywordAuthorgenerative adversarial networks (GANs)-
dc.subject.keywordAuthormedian filtering (MF)-
dc.subject.keywordPlusDIGITAL IMAGES-
dc.subject.keywordPlusTRACES-
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 18 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0