For hundreds of years, paper and printing techniques are the foundation for distribution of information. Thereby, physical documents enjoy high confidence. To have something in „black and white“ carries significance. But forgery or alteration of documents are nearly as old as the history of writing. Hence the question for the originator is important. Nowadays, because of continual technological development, even individuals can print high-quality documents and duplicates. This can be done with cheap and customary devices. In addition image processing software, like gimp or photoshop, allows to manipulate documents and images easily. Therefore printed documents are often an issue in crimes, e.g. faked proof of identity, copyright theft, or as exhibit in a criminal case. In all this cases it could be useful for law enforcement agencies to, e.g., identify the device or model which was used to print the questionable document.
In the first part of the project we will investigate the area of printer forensics. Thereby especially the identification of the printing technology and the printer (brand, model, device itself) will be analysed. The primary focus of the research lies on the extraction of artefacts, which are originated from the print process. These artefacts emerge particularly through electromechanical imperfections and also through differences between constructions of printer models. We try to find and analyse artefacts which are stable and e.g. model specific and therefore usable as intrinsic signatures for a specific printer model. The extraction of these signatures should be conducted with customary scanner devices and image processing techniques. Simultaneously, the company Dence (cooperation partner) will investigate the scanner forensics.
The second part of the project deals with research of print-scan resilient checks of duplicates. Here the knowledge about artefacts which occur in the print and scan process represent a reliable basis. As use case the typical workflow in an insurance can be mentioned. In the event of damage, the insured person would take a photo of the damage and send it postal, together with forms, to the insurance. Afterwards the insurance will scan these documents. The insurance staff examines the event of damage on the basis of the digital reproduction. Because of the print and scan processes, which the original document went through, the question of authenticity of the document is difficulty to answer. Goal of the analysis is the implementation of an image search for duplicates, which is resilient against the print and scan process. This means, that images can also be matched, if only printed and re-digitized versions are available. The duplicate testing must be able to check an image immediately against a data pool with millions of images. From technical view, this will be realised with a distinct image hash. The challenge for the print-scan scenario is the computation of this hash. The hash value has to be equal or very similar between the original photo and the printed and re-digitized image, so that they can matched. Another difficulty is a working check for duplicates also with slight manpulations in the image (like a car scratch). All of this must be accomplished without matching false positives.
Stephan Escher, Thorsten Strufe. Robustness analysis of a passive printer identification scheme for halftone images. In IEEE International Conference on Image Processing (ICIP), 2017
Timo Richter* , Stephan Escher* , Dagmar Schönfeld, Thorsten Strufe. Forensic Analysis and Anonymisation of Printed Documents. In Proceedings of 6th ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec ’18), 2018
Description: The deda toolkit enables automatic extraction, decoding and anonymisation of document colour tracking dots
If you use this software, please cite the paper:
Timo Richter*, Stephan Escher*, Dagmar Schönfeld and Thorsten Strufe. 2018. Forensic Analysis and Anonymisation of Printed Documents. In Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec '18). ACM, New York, NY, USA, 127-138. DOI
Example for Ubuntu 16.10 or newer
Example for Windows 10
Description: The Modular Image Hashing Benchmarking System MIHBS makes it possible to compare and evaluate perceptual hashing approaches regarding their robustness against specific perceptual preserving transformations, their sensitivity and specific labeled application datasets.