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LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS

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Citation Author(s):
Owen Mayer, Mathew C. Stamm
Submitted by:
Owen Mayer
Last updated:
27 April 2018 - 12:45pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Owen Mayer
Paper Code:
3283
Categories:

Abstract 

Abstract: 

Information about an image's source camera model is important knowledge in many forensic investigations. In this paper we propose a system that compares two image patches to determine if they were captured by the same camera model. To do this, we first train a CNN based feature extractor to output generic, high level features which encode information about the source camera model of an image patch. Then, we learn a similarity measure that maps pairs of these features to a score indicating whether the two image patches were captured by the same or different camera models. We show that our proposed system accurately determines if two patches were captured by the same or different camera models, even when the camera models are unknown to the investigator. We also demonstrate the utility of this approach for image splicing detection and localization

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