A System Based on Intrinsic Features for Fraudulent Document Detection

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.

[1]  Thomas M. Breuel,et al.  Automatic Line Orientation Measurement for Questioned Document Examination , 2009, IWCF.

[2]  Faisal Shafait,et al.  Distortion Measurement for Automatic Document Verification , 2011, 2011 International Conference on Document Analysis and Recognition.

[3]  Jan P. Allebach,et al.  Data Hiding Capacity and Embedding Techniques for Printed Text Documents , 2006, NIP & Digital Fabrication Conference.

[4]  Lin Mei,et al.  Printing Technique Classification for Document Counterfeit Detection , 2006, 2006 International Conference on Computational Intelligence and Security.

[5]  Thomas M. Breuel,et al.  Document inspection using text-line alignment , 2010, DAS '10.

[6]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[7]  Jan P. Allebach,et al.  Printer identification based on texture features , 2004 .

[8]  Thomas M. Breuel,et al.  Document Signature Using Intrinsic Features for Counterfeit Detection , 2008, IWCF.

[9]  Christian Riess,et al.  Ieee Transactions on Information Forensics and Security an Evaluation of Popular Copy-move Forgery Detection Approaches , 2022 .

[10]  Ernest Valveny,et al.  A Review of Shape Descriptors for Document Analysis , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[11]  Robert M. Haralick,et al.  Global and local document degradation models , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[12]  Thomas M. Breuel,et al.  Using DCT Features for Printing Technique and Copy Detection , 2009, IFIP Int. Conf. Digital Forensics.

[13]  Thomas M. Breuel.  Robust least-square-baseline finding using a branch and bound algorithm , 2001, IS&T/SPIE Electronic Imaging.

[14]  R. L. van Renesse,et al.  Paper based document security-a review , 1997 .