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Due to missing first line inspection in many automated digitization setups, it has become more difficult to identify forged documents. Widespread availability of high-quality printing and scanning devices have further elevated the problem by enabling even non-experts to generate high-quality forgeries. When training a machine learning system for forgery detection, one is faced with several challenges like unbalanced classes, or even absence of one class (no real forgeries might be available to train the system).

The AnDruDok project aims at bringing together research in document forensics and anomaly detection for identifying suspicious documents in a document collection. The main objective in this project is to investigate unsupervised machine learning techniques for forgery detection in document images. Particularly, the approaches based on modeling class distributions will be investigated to develop algorithms that can detect forged documents as outliers in the document collection.

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Publications about the project

PhD-Thesis Technische Universität Kaiserslautern ISBN 978-3-8439-1572-4 Dr. Hut München 2/2014.

To the publication
Slim Abdennadher,

In: Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description (ODD). International Conference on Knowledge Discovery and Data Mining (KDD-2013) August 11-14 Chicago IL United States Pages 8-15 ISBN 978-1-4503-2335-2 ACM New York, NY, USA 8/2013.

To the publication
Andreas Dengel

In: Proceedings of the 12th International Conference on Document Analysis and Recognition. International Conference on Document Analysis and Recognition (ICDAR-2013) 12th August 25-28 Washington DC United States Pages 479-483 ISBN 978-0-7695-4999-3 IEEE Computer Society 8/2013.

To the publication

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz