2013 · 54 citations · 7 references
Fraud DetectionEngineeringInformation SecurityBiometricsInformation ForensicsImage ForensicsText MiningInformation RetrievalData ScienceData MiningPattern RecognitionCharacter RecognitionOptical Character RecognitionKnowledge DiscoveryComputer ScienceDigital WatermarkingOutlier Character DetectionContent Similarity DetectionPaper DocumentsDigital ForensicsIntrinsic FeaturesArtsDocument Processing
Paper documents still represent a large amount of information supports used nowadays and may contain critical data. Even though official documents are secured with techniques such as printed patterns or artwork, paper documents suffer from a lack of security. However, the high availability of cheap scanning and printing hardware allows non-experts to easily create fake documents. As the use of a watermarking system added during the document production step is hardly possible, solutions have to be proposed to distinguish a genuine document from a forged one. In this paper, we present an automatic forgery detection method based on document's intrinsic features at character level. This method is based on the one hand on outlier character detection in a discriminant feature space and on the other hand on the detection of strictly similar characters. Therefore, a feature set is computed for all characters. Then, based on a distance between characters of the same class, the character is classified as a genuine one or a fake one.
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Visual pattern recognition by moment invariants
Ming-Kuei Hu · IEEE Transactions on Information Theory · 1962 · 7.5K citations
Global and local document degradation models
Tapas Kanungo, R.M. Haralick, Ihsin T. Phillips · 2002 · 141 citations
<title>Robust least-square-baseline finding using a branch and bound algorithm</title>
Thomas M. Breuel · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001 · 36 citations · Full text
Document Processing, Image Analysis, Robust Least-square-baseline +13