Image forgery detection via forensic similarity graphs
Resumen:
In the article 'Exposing Fake Images with Forensic Similarity Graphs', O. Mayer and M. C. Stamm introduce a novel image forgery detection method. The proposed method is built on a graph-based representation of images, where image patches are represented as the vertices of the graph, and the edge weights are assigned in order to reflect the forensic similarity between the connected patches. In this representation, forged regions form highly connected subgraphs. Therefore, forgery detection and localization can be cast as a cluster analysis problem on the similarity graph. The authors present two graph clustering methods to detect and localize image forgeries. In this paper, we present briefly the method and offer an online executable version allowing everyone to test it on their own suspicious images.
| 2022 | |
|
Projecto ANR-16-DEFA-0004 Proyecto vera.ai (101070093) |
|
|
Image forensics Forgery detection Graph clustering |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
|
http://www.ipol.im/pub/art/2022/432/
https://hdl.handle.net/20.500.12008/39790 |
|
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
| _version_ | 1807522936355028992 |
|---|---|
| author | Gardella, Marina |
| author2 | Musé, Pablo |
| author2_role | author |
| author_facet | Gardella, Marina Musé, Pablo |
| author_role | author |
| bitstream.checksum.fl_str_mv | 6429389a7df7277b72b7924fdc7d47a9 a9ac1bac94fe38dbe560422d834a993f 53784372e43d38a8ceff0d19b14b7614 6a69abe32f6fabdffa4c61be8f8efebd 95d5f2fce45bf48d4d2ea58d991ee725 |
| bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 MD5 MD5 MD5 |
| bitstream.url.fl_str_mv | http://localhost:8080/xmlui/bitstream/20.500.12008/39790/5/license.txt http://localhost:8080/xmlui/bitstream/20.500.12008/39790/2/license_url http://localhost:8080/xmlui/bitstream/20.500.12008/39790/3/license_text http://localhost:8080/xmlui/bitstream/20.500.12008/39790/4/license_rdf http://localhost:8080/xmlui/bitstream/20.500.12008/39790/1/GM22a.pdf |
| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Gardella Marina, Université Paris-Saclay, France Musé Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.creator.none.fl_str_mv | Gardella, Marina Musé, Pablo |
| dc.date.accessioned.none.fl_str_mv | 2023-09-05T12:32:18Z |
| dc.date.available.none.fl_str_mv | 2023-09-05T12:32:18Z |
| dc.date.issued.none.fl_str_mv | 2022 |
| dc.description.abstract.none.fl_txt_mv | In the article 'Exposing Fake Images with Forensic Similarity Graphs', O. Mayer and M. C. Stamm introduce a novel image forgery detection method. The proposed method is built on a graph-based representation of images, where image patches are represented as the vertices of the graph, and the edge weights are assigned in order to reflect the forensic similarity between the connected patches. In this representation, forged regions form highly connected subgraphs. Therefore, forgery detection and localization can be cast as a cluster analysis problem on the similarity graph. The authors present two graph clustering methods to detect and localize image forgeries. In this paper, we present briefly the method and offer an online executable version allowing everyone to test it on their own suspicious images. |
| dc.description.sponsorship.none.fl_txt_mv | Projecto ANR-16-DEFA-0004 Proyecto vera.ai (101070093) |
| dc.format.extent.es.fl_str_mv | 11 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Gardella, M. y Musé, P. "Image forgery detection via forensic similarity graphs". IPOL. Journal Image Processing On Line. [en línea]. 2022, no 12, pp. 490-500. DOI: 10.5201/ipol.2022.432. |
| dc.identifier.doi.none.fl_str_mv | 10.5201/ipol.2022.432 |
| dc.identifier.issn.none.fl_str_mv | 2105–1232 |
| dc.identifier.uri.none.fl_str_mv | http://www.ipol.im/pub/art/2022/432/ https://hdl.handle.net/20.500.12008/39790 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | ENS Paris-Saclay. Centre Borelli : Universitat de les Illes Balears. DMI : Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.relation.ispartof.es.fl_str_mv | IPOL. Journal Image Processing On Line, no. 12, Nov. 2022, pp. 490-500. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Image forensics Forgery detection Graph clustering |
| dc.title.none.fl_str_mv | Image forgery detection via forensic similarity graphs |
| dc.type.es.fl_str_mv | Artículo |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | In the article 'Exposing Fake Images with Forensic Similarity Graphs', O. Mayer and M. C. Stamm introduce a novel image forgery detection method. The proposed method is built on a graph-based representation of images, where image patches are represented as the vertices of the graph, and the edge weights are assigned in order to reflect the forensic similarity between the connected patches. In this representation, forged regions form highly connected subgraphs. Therefore, forgery detection and localization can be cast as a cluster analysis problem on the similarity graph. The authors present two graph clustering methods to detect and localize image forgeries. In this paper, we present briefly the method and offer an online executable version allowing everyone to test it on their own suspicious images. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | COLIBRI_1a855e1c4eb8db440240f5096cc49190 |
| identifier_str_mv | Gardella, M. y Musé, P. "Image forgery detection via forensic similarity graphs". IPOL. Journal Image Processing On Line. [en línea]. 2022, no 12, pp. 490-500. DOI: 10.5201/ipol.2022.432. 2105–1232 10.5201/ipol.2022.432 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/39790 |
| publishDate | 2022 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0) |
| spelling | Gardella Marina, Université Paris-Saclay, FranceMusé Pablo, Universidad de la República (Uruguay). Facultad de Ingeniería.2023-09-05T12:32:18Z2023-09-05T12:32:18Z2022Gardella, M. y Musé, P. "Image forgery detection via forensic similarity graphs". IPOL. Journal Image Processing On Line. [en línea]. 2022, no 12, pp. 490-500. DOI: 10.5201/ipol.2022.432.2105–1232http://www.ipol.im/pub/art/2022/432/https://hdl.handle.net/20.500.12008/3979010.5201/ipol.2022.432In the article 'Exposing Fake Images with Forensic Similarity Graphs', O. Mayer and M. C. Stamm introduce a novel image forgery detection method. The proposed method is built on a graph-based representation of images, where image patches are represented as the vertices of the graph, and the edge weights are assigned in order to reflect the forensic similarity between the connected patches. In this representation, forged regions form highly connected subgraphs. Therefore, forgery detection and localization can be cast as a cluster analysis problem on the similarity graph. The authors present two graph clustering methods to detect and localize image forgeries. In this paper, we present briefly the method and offer an online executable version allowing everyone to test it on their own suspicious images.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2023-09-01T23:38:10Z No. of bitstreams: 2 license_rdf: 23749 bytes, checksum: 6a69abe32f6fabdffa4c61be8f8efebd (MD5) GM22a.pdf: 6763983 bytes, checksum: 95d5f2fce45bf48d4d2ea58d991ee725 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2023-09-04T18:52:42Z (GMT) No. of bitstreams: 2 license_rdf: 23749 bytes, checksum: 6a69abe32f6fabdffa4c61be8f8efebd (MD5) GM22a.pdf: 6763983 bytes, checksum: 95d5f2fce45bf48d4d2ea58d991ee725 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2023-09-05T12:32:18Z (GMT). No. of bitstreams: 2 license_rdf: 23749 bytes, checksum: 6a69abe32f6fabdffa4c61be8f8efebd (MD5) GM22a.pdf: 6763983 bytes, checksum: 95d5f2fce45bf48d4d2ea58d991ee725 (MD5) Previous issue date: 2022Projecto ANR-16-DEFA-0004Proyecto vera.ai (101070093)11 p.application/pdfenengENS Paris-Saclay. Centre Borelli : Universitat de les Illes Balears. DMI : Universidad de la República (Uruguay). Facultad de Ingeniería.IPOL. Journal Image Processing On Line, no. 12, Nov. 2022, pp. 490-500.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Compartir Igual (CC - By-NC-SA 4.0)Image forensicsForgery detectionGraph clusteringImage forgery detection via forensic similarity graphsArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaGardella, MarinaMusé, PabloProcesamiento de SeñalesTratamiento de ImágenesLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/39790/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-850http://localhost:8080/xmlui/bitstream/20.500.12008/39790/2/license_urla9ac1bac94fe38dbe560422d834a993fMD52license_textlicense_texttext/html; charset=utf-839036http://localhost:8080/xmlui/bitstream/20.500.12008/39790/3/license_text53784372e43d38a8ceff0d19b14b7614MD53license_rdflicense_rdfapplication/rdf+xml; charset=utf-823749http://localhost:8080/xmlui/bitstream/20.500.12008/39790/4/license_rdf6a69abe32f6fabdffa4c61be8f8efebdMD54ORIGINALGM22a.pdfGM22a.pdfapplication/pdf6763983http://localhost:8080/xmlui/bitstream/20.500.12008/39790/1/GM22a.pdf95d5f2fce45bf48d4d2ea58d991ee725MD5120.500.12008/397902024-07-24 17:25:47.492oai:colibri.udelar.edu.uy:20.500.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Universidadhttps://udelar.edu.uy/https://www.colibri.udelar.edu.uy/oai/requestmabel.seroubian@seciu.edu.uyUruguayopendoar:47712024-07-25T14:33:33.205374COLIBRI - Universidad de la Repúblicafalse |
| spellingShingle | Image forgery detection via forensic similarity graphs Gardella, Marina Image forensics Forgery detection Graph clustering |
| status_str | publishedVersion |
| title | Image forgery detection via forensic similarity graphs |
| title_full | Image forgery detection via forensic similarity graphs |
| title_fullStr | Image forgery detection via forensic similarity graphs |
| title_full_unstemmed | Image forgery detection via forensic similarity graphs |
| title_short | Image forgery detection via forensic similarity graphs |
| title_sort | Image forgery detection via forensic similarity graphs |
| topic | Image forensics Forgery detection Graph clustering |
| url | http://www.ipol.im/pub/art/2022/432/ https://hdl.handle.net/20.500.12008/39790 |
