Document Signature Using Intrinsic Features for Counterfeit Detection
Identifieur interne : 000711 ( Istex/Checkpoint ); précédent : 000710; suivant : 000712Document Signature Using Intrinsic Features for Counterfeit Detection
Auteurs : Joost Van Beusekom [Allemagne] ; Faisal Shafait [Allemagne] ; M. Breuel [Allemagne]Source :
- 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.
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DOI: 10.1007/978-3-540-85303-9_5
Affiliations:
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<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>
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