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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Fighting Fake News: Image Splice Detection via Learned Self-Consistency

verfasst von : Minyoung Huh, Andrew Liu, Andrew Owens, Alexei A. Efros

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

Advances in photo editing and manipulation tools have made it significantly easier to create fake imagery. Learning to detect such manipulations, however, remains a challenging problem due to the lack of sufficient amounts of manipulated training data. In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs. The algorithm uses the automatically recorded photo EXIF metadata as supervisory signal for training a model to determine whether an image is self-consistent — that is, whether its content could have been produced by a single imaging pipeline. We apply this self-consistency model to the task of detecting and localizing image splices. The proposed method obtains state-of-the-art performance on several image forensics benchmarks, despite never seeing any manipulated images at training. That said, it is merely a step in the long quest for a truly general purpose visual forensics tool.

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Vorheriges Kapitel Image Inpainting for Irregular Holes Using Partial Convolutions
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1
F1 score is defined as \(\frac{2TP}{2TP + FN + FP}\) and MCC as \(\frac{(TP \times TN) - (FP \times FN)}{\sqrt{(TP + FP)(TP + FN)(TN + FP)(TN + FN)}}\).
 
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Metadaten
Titel
Fighting Fake News: Image Splice Detection via Learned Self-Consistency
verfasst von
Minyoung Huh
Andrew Liu
Andrew Owens
Alexei A. Efros
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01251-9

Electronic ISBN: 978-3-030-01252-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01252-6_7

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