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Detecting double compression and splicing using benfords first digit law

: Frick, Raphael Antonius; Liu, Huajian; Steinebach, Martin


Volkamer, M. ; Association for Computing Machinery -ACM-:
ARES 2020, 15th International Conference on Availability, Reliability and Security : August 25 - August 28, 2020, All-digital Conference
New York: ACM, 2020
ISBN: 978-1-4503-8833-7
Art. 47, 9 pp.
International Conference on Availability, Reliability and Security (ARES) <15, 2020, Online>
Conference Paper
Fraunhofer SIT ()

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.