Distortion Measurement for Automatic Document Verification
Document forgery detection is important as techniques to generate forgeries are becoming widely available and easy to use even for untrained persons. In this work, two types of forgeries are considered: forgeries generated by reengineering a document and forgeries that are generated using scanning and printing a genuine document. An unsupervised approach is presented to automatically detect forged documents of these types by detecting the geometric distortions introduced during the forgery process. Using the matching quality between all pairs of documents, outlier detection is performed on the summed matching quality to identify the tampered document. Quantitative evaluation is done on two public data sets, reporting a true positive rate from to 0.7 to 1.0.
Joost van Beusekom Faisal Shafait Multimedia Analysis Data Mining Group
German Research Center for Artificial Intelligence (DFKI)Kaiserslautern, Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern, Germany
国际会议
北京
英文
289-293
2011-09-01（万方平台首次上网日期，不代表论文的发表时间）