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arXiv · 1805.04096

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

Minyoung Huh·Andrew Liu·Andrew Owens·Alexei A. Efros
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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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Minyoung Huh, Andrew Liu, Andrew Owens, Alexei A. Efros. 2018-05-10. Fighting Fake News: Image Splice Detection via Learned Self-Consistency. https://arxiv.org/abs/1805.04096

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