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  4. Detecting double compression and splicing using benfords first digit law
 
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2020
Conference Paper
Title

Detecting double compression and splicing using benfords first digit law

Abstract
Detecting image forgeries in JPEG encoded images has been a research topic in the field of media forensics for a long time. Until today, it still holds a high importance as tools to create convincing manipulations of images have become more and more accessible to the public, which in return might be used to e.g. generate fake news. In this paper, a passive forensic detection framework to detect image manipulations is proposed based on compression artefacts and Benfords First Digit Law. It incorporates a supervised approach to reconstruct the compression history as well as provides an un-supervised detection approach to detect double compression for unknown quantization tables. The implemented algorithms were able to achieve high AUC values when classifying high quality images exceeding similar state-of-the-art methods.
Author(s)
Frick, Raphael Antonius
Liu, Huajian  
Steinebach, Martin  
Mainwork
ARES 2020, 15th International Conference on Availability, Reliability and Security  
Conference
International Conference on Availability, Reliability and Security (ARES) 2020  
DOI
10.1145/3407023.3409200
Language
English
Fraunhofer-Institut für Sichere Informationstechnologie SIT  
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