Categorization of Document Image Tampering Techniques and How to Identify Them
We present in a descriptive way the first results of our study of the problem of document image tampering detection. We aim at helping the community by establishing certain guidelines in what refers to the categorization and targeting of this problem. We propose a categorization of the main types of forgeries performed by a direct manipulation of the document image. That applies to most of the cases we observed in real world forged documents according to our sources from external private companies. In addition, we describe a set of visual clues result of these tampering operations that can be addressed when developing automatic methods for its detection.
KeywordsForensics Document security Document analysis
This project has been granted by the Region Nouvelle Aquitaine and European Union supporting the project “Securdoc: développement d’un prototype de détection de fraude de document numérique” framed at the “programme opérationnel FEDER/FSE 2014–2020” (grant number P2016-BAFE-186).
- 2.Tkachenko, I., Gomez-Krmer, P.: Robustness of character recognition techniques to double print-and-scan process. In: 14th IAPR International Conference on Document Analysis and Recognition, ICDAR, vol. 09, pp. 27–32, November 2017Google Scholar
- 3.Prabhu, A.S., Shah, Z., Shah, M.: Robust detection of copy move forgeries for scanned documents using multiple methods. Int. J. Comput. Sci. Issues 9, 436 (2012)Google Scholar
- 4.Malik, M.I., et al.: Proceedings of the 2nd ICDAR International Workshop on Automated Forensic Handwriting Analysis, AFHA 2013, Washington DC, USA, 22–23 August 2013, vol. 1022 (2013)Google Scholar
- 5.Bertrand, R., Gomez-Krãmer, P., Terrades, O.R., Franco, P., Ogier, J.-M.: A system based on intrinsic features for fraudulent document detection. In: 12th International Conference on Document Analysis and Recognition, Washington, DC, United States, vol. 12, pp. 106–110 (2013)Google Scholar
- 9.Cruz, F., Sidre, N., Coustaty, M., Poulain D’Andecy, V., Ogier, J.M.: Local binary patterns for document forgery detection. In 14th IAPR International Conference on Document Analysis and Recognition, ICDAR, pp. 1223–1228, November 2017Google Scholar