Mullan P, Riess C, Freiling F (2020)
Publication Type: Journal article
Publication year: 2020
Book Volume: 32
Journal Issue: S
DOI: 10.1016/j.fsidi.2020.300916
Image provenance, i.e., information on the model and make of the device that was used to produce an image, is a helpful cue in many digital investigations. Such information can, for example, help refute the hypothesis that an illegal photograph found on the Internet was produced using the personal device of a suspect. Grouping images by provenance can be done in different ways. Based on the encouraging insights from previous works, we consider the grouping of JPEG images by their file headers where previous work performed image classification on a closed-set of devices. However, due to the ongoing development of new camera models and the practical difficulty to keep a model database up-to-date, we propose to treat image provenance as an open-set classification problem with the goal to predict the make of a previously unseen camera model. We show that such a prediction can performe remarkably well, with median accuracies beyond 90% for each make. In a more challenging experiment with images that were postprocessed, we achieve a median accuracy of about 55% and 75% for desktop software and for smartphone apps, respectively. (C) 2020 The Author(s). Published by Elsevier Ltd.
APA:
Mullan, P., Riess, C., & Freiling, F. (2020). Towards Open-Set Forensic Source Grouping on JPEG Header Information. Forensic Science International: Digital Investigation, 32(S). https://dx.doi.org/10.1016/j.fsidi.2020.300916
MLA:
Mullan, Patrick, Christian Riess, and Felix Freiling. "Towards Open-Set Forensic Source Grouping on JPEG Header Information." Forensic Science International: Digital Investigation 32.S (2020).
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