The Forchheim Image Database for Camera Identification in the Wild

Hadwiger B, Rieß C (2021)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2021

Event location: Mailand IT

URI: https://faui1-files.cs.fau.de/public/publications/mmsec/2021-Hadwiger-FDB.pdf

DOI: 10.1007/978-3-030-68780-9_40

Abstract

Image provenance can represent crucial knowledge
in criminal investigation and journalistic fact checking. In the
last two decades, numerous algorithms have been proposed for
obtaining information on the source camera and distribution
history of an image. For a fair ranking of these techniques, it is
important to rigorously assess their performance on practically
relevant test cases. To this end, a number of datasets have been
proposed. However, we argue that there is a gap in existing
databases: to our knowledge, there is currently no dataset that
simultaneously satisfies two goals, namely a) to cleanly separate
scene content and forensic traces, and b) to support realistic
post-processing like social media recompression.
In this work, we propose the Forchheim Image Database
(FODB) to close this gap. It consists of more than 23,000 images
of 143 scenes by 27 smartphone cameras, and it allows to cleanly
separate image content from forensic artifacts. Each image is
provided in 6 different qualities: the original camera-native
version, and five copies from social networks. We demonstrate
the usefulness of FODB in an evaluation of methods for cam-
era identification. We report three findings. First, the recently
proposed general-purpose EfficientNet remarkably outperforms
several dedicated forensic CNNs both on clean and compressed
images. Second, classifiers obtain a performance boost even on
unknown post-processing after augmentation by artificial degra-
dations. Third, FODB’s clean separation of scene content and
forensic traces imposes important, rigorous boundary conditions
for algorithm benchmarking.

Authors with CRIS profile

Benjamin Hadwiger Lehrstuhl für Informatik 1 (IT-Sicherheitsinfrastrukturen) Christian Rieß Lehrstuhl für Informatik 1 (IT-Sicherheitsinfrastrukturen)

How to cite

APA:

Hadwiger, B., & Rieß, C. (2021). The Forchheim Image Database for Camera Identification in the Wild. In Proceedings of the Multimedia Forensics in the Wild (MMForWild 2020). Mailand, IT.

MLA:

Hadwiger, Benjamin, and Christian Rieß. "The Forchheim Image Database for Camera Identification in the Wild." Proceedings of the Multimedia Forensics in the Wild (MMForWild 2020), Mailand 2021.

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