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https://dx.doi.org/10.48550/ar...
Article . 2019
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
DBLP
Article . 2019
Data sources: DBLP
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Differential Imaging Forensics

Authors: Aurélien Bourquard; Jeff Yan;

Differential Imaging Forensics

Abstract

We introduce some new forensics based on differential imaging, where a novel category of visual evidence created via subtle interactions of light with a scene, such as dim reflections, can be computationally extracted and amplified from an image of interest through a comparative analysis with an additional reference baseline image acquired under similar conditions. This paradigm of differential imaging forensics (DIF) enables forensic examiners for the first time to retrieve the said visual evidence that is readily available in an image or video footage but would otherwise remain faint or even invisible to a human observer. We demonstrate the relevance and effectiveness of our approach through practical experiments. We also show that DIF provides a novel method for detecting forged images and video clips, including deep fakes.

Keywords

FOS: Computer and information sciences, Computer Science - Cryptography and Security, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Cryptography and Security (cs.CR), Computer Science - Multimedia, Multimedia (cs.MM)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average