Enhancing one-class support vector machines for unsupervised anomaly detection
Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description
| Hi-index | 0.00 |
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 re-engineering 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.