Document Signature Using Intrinsic Features for Counterfeit Detection
Identifieur interne : 000886 ( Istex/Corpus ); précédent : 000885; suivant : 000887Document Signature Using Intrinsic Features for Counterfeit Detection
Auteurs : Joost Van Beusekom ; Faisal Shafait ; M. BreuelSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
Abstract: Document security does not only play an important role in specific domains e.g. passports, checks and degrees but also in every day documents e.g. bills and vouchers. Using special high-security features for this class of documents is not feasible due to the cost and the complexity of these methods. We present an approach for detecting falsified documents using a document signature obtained from its intrinsic features: bounding boxes of connected components are used as a signature. Using the model signature learned from a set of original bills, our approach can identify documents whose signature significantly differs from the model signature. Our approach uses globally optimal document alignment to build a model signature that can be used to compute the probability of a new document being an original one. Preliminary evaluation shows that the method is able to reliably detect faked documents.
Url:
DOI: 10.1007/978-3-540-85303-9_5
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Breuel</name><affiliation><mods:affiliation>Technical University of Kaiserslautern, Kaiserslautern, Germany</mods:affiliation></affiliation><affiliation><mods:affiliation>German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: tmb@dfki.uni-kl.de</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2008</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">1F1888D29239A5E61CB93EBC7B96F4C5C62F5F2D</idno><idno type="DOI">10.1007/978-3-540-85303-9_5</idno><idno type="ChapterID">5</idno><idno type="ChapterID">Chap5</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Document security does not only play an important role in specific domains e.g. passports, checks and degrees but also in every day documents e.g. bills and vouchers. Using special high-security features for this class of documents is not feasible due to the cost and the complexity of these methods. We present an approach for detecting falsified documents using a document signature obtained from its intrinsic features: bounding boxes of connected components are used as a signature. Using the model signature learned from a set of original bills, our approach can identify documents whose signature significantly differs from the model signature. Our approach uses globally optimal document alignment to build a model signature that can be used to compute the probability of a new document being an original one. Preliminary evaluation shows that the method is able to reliably detect faked documents.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Joost van Beusekom</name><affiliations><json:string>Technical University of Kaiserslautern, Kaiserslautern, Germany</json:string><json:string>E-mail: joost.van-beusekom@dfki.uni-kl.de</json:string></affiliations></json:item><json:item><name>Faisal Shafait</name><affiliations><json:string>German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany</json:string><json:string>E-mail: faisal.shafait@dfki.uni-kl.de</json:string></affiliations></json:item><json:item><name>Thomas M. 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Using the model signature learned from a set of original bills, our approach can identify documents whose signature significantly differs from the model signature. Our approach uses globally optimal document alignment to build a model signature that can be used to compute the probability of a new document being an original one. 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Using special high-security features for this class of documents is not feasible due to the cost and the complexity of these methods. We present an approach for detecting falsified documents using a document signature obtained from its intrinsic features: bounding boxes of connected components are used as a signature. Using the model signature learned from a set of original bills, our approach can identify documents whose signature significantly differs from the model signature. Our approach uses globally optimal document alignment to build a model signature that can be used to compute the probability of a new document being an original one. 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Proceedings</BookSubTitle><BookVolumeNumber>5158</BookVolumeNumber><BookSequenceNumber>5158</BookSequenceNumber><BookDOI>10.1007/978-3-540-85303-9</BookDOI><BookTitleID>177648</BookTitleID><BookPrintISBN>978-3-540-85302-2</BookPrintISBN><BookElectronicISBN>978-3-540-85303-9</BookElectronicISBN><BookChapterCount>20</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2008</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I22040" Priority="1" Type="Secondary">Biometrics</BookSubject><BookSubject Code="I2203X" Priority="2" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I22021" Priority="3" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I18030" Priority="4" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><BookSubject Code="I22005" Priority="5" Type="Secondary">Computer Imaging, Vision, Pattern Recognition and Graphics</BookSubject><BookSubject Code="I21041" Priority="6" Type="Secondary">Language Translation and Linguistics</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup></BookInfo><BookHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>Sargur</GivenName><GivenName>N.</GivenName><FamilyName>Srihari</FamilyName></EditorName><Contact><Email>srihari@cedar.buffalo.edu</Email></Contact></Editor><Editor><EditorName DisplayOrder="Western"><GivenName>Katrin</GivenName><FamilyName>Franke</FamilyName></EditorName><Contact><Email>kyfranke@hig.no</Email></Contact></Editor></EditorGroup></BookHeader><Part ID="Part2"><PartInfo TocLevels="0"><PartID>2</PartID><PartSequenceNumber>2</PartSequenceNumber><PartTitle>Scanner, Printer, and Prints</PartTitle><PartChapterCount>3</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Computational Forensics</BookTitle></PartContext></PartInfo><Chapter ID="Chap5" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>5</ChapterID><ChapterDOI>10.1007/978-3-540-85303-9_5</ChapterDOI><ChapterSequenceNumber>5</ChapterSequenceNumber><ChapterTitle Language="En">Document Signature Using Intrinsic Features for Counterfeit Detection</ChapterTitle><ChapterFirstPage>47</ChapterFirstPage><ChapterLastPage>57</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2008</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>2</PartID><BookID>978-3-540-85303-9</BookID><BookTitle>Computational Forensics</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Joost</GivenName><Particle>van</Particle><FamilyName>Beusekom</FamilyName></AuthorName><Contact><Email>joost.van-beusekom@dfki.uni-kl.de</Email><URL>http://www.iupr.org</URL></Contact></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Faisal</GivenName><FamilyName>Shafait</FamilyName></AuthorName><Contact><Email>faisal.shafait@dfki.uni-kl.de</Email></Contact></Author><Author AffiliationIDS="Aff1 Aff2"><AuthorName DisplayOrder="Western"><GivenName>Thomas</GivenName><GivenName>M.</GivenName><FamilyName>Breuel</FamilyName></AuthorName><Contact><Email>tmb@dfki.uni-kl.de</Email></Contact></Author><Affiliation ID="Aff1"><OrgName>Technical University of Kaiserslautern</OrgName><OrgAddress><City>Kaiserslautern</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>German Research Center for Artificial Intelligence (DFKI)</OrgName><OrgAddress><City>Kaiserslautern</City><Country>Germany</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Document security does not only play an important role in specific domains e.g. passports, checks and degrees but also in every day documents e.g. bills and vouchers. Using special high-security features for this class of documents is not feasible due to the cost and the complexity of these methods. We present an approach for detecting falsified documents using a document signature obtained from its intrinsic features: bounding boxes of connected components are used as a signature. Using the model signature learned from a set of original bills, our approach can identify documents whose signature significantly differs from the model signature. Our approach uses globally optimal document alignment to build a model signature that can be used to compute the probability of a new document being an original one. Preliminary evaluation shows that the method is able to reliably detect faked documents.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Document Signature Using Intrinsic Features for Counterfeit Detection</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Document Signature Using Intrinsic Features for Counterfeit Detection</title></titleInfo><name type="personal"><namePart type="given">Joost</namePart><namePart type="family">van Beusekom</namePart><affiliation>Technical University of Kaiserslautern, Kaiserslautern, Germany</affiliation><affiliation>E-mail: joost.van-beusekom@dfki.uni-kl.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Faisal</namePart><namePart type="family">Shafait</namePart><affiliation>German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany</affiliation><affiliation>E-mail: faisal.shafait@dfki.uni-kl.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Thomas</namePart><namePart type="given">M.</namePart><namePart type="family">Breuel</namePart><affiliation>Technical University of Kaiserslautern, Kaiserslautern, Germany</affiliation><affiliation>German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany</affiliation><affiliation>E-mail: tmb@dfki.uni-kl.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2008</dateIssued><copyrightDate encoding="w3cdtf">2008</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Document security does not only play an important role in specific domains e.g. passports, checks and degrees but also in every day documents e.g. bills and vouchers. Using special high-security features for this class of documents is not feasible due to the cost and the complexity of these methods. We present an approach for detecting falsified documents using a document signature obtained from its intrinsic features: bounding boxes of connected components are used as a signature. Using the model signature learned from a set of original bills, our approach can identify documents whose signature significantly differs from the model signature. Our approach uses globally optimal document alignment to build a model signature that can be used to compute the probability of a new document being an original one. Preliminary evaluation shows that the method is able to reliably detect faked documents.</abstract><relatedItem type="host"><titleInfo><title>Computational Forensics</title><subTitle>Second International Workshop, IWCF 2008, Washington, DC, USA, August 7-8, 2008. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Sargur</namePart><namePart type="given">N.</namePart><namePart type="family">Srihari</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Katrin</namePart><namePart type="family">Franke</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2008</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I22040">Biometrics</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I18030">Data Mining and Knowledge Discovery</topic><topic authority="SpringerSubjectCodes" authorityURI="I22005">Computer Imaging, Vision, Pattern Recognition and Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I21041">Language Translation and Linguistics</topic></subject><identifier type="DOI">10.1007/978-3-540-85303-9</identifier><identifier type="ISBN">978-3-540-85302-2</identifier><identifier type="eISBN">978-3-540-85303-9</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">177648</identifier><identifier type="BookID">978-3-540-85303-9</identifier><identifier type="BookChapterCount">20</identifier><identifier type="BookVolumeNumber">5158</identifier><identifier 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