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

Minyoung Huh*12
Andrew Liu*1
Andrew Owens1
Alexei A. Efros1
1UC Berkeley
2Carnegie Mellon University

Code [GitHub]

ECCV 2018 [Paper]

[Slides]



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.

Video

[Slides (164 MB)][PDF Slides (7 MB)]

EXIF Consistency Training

Try our code! [GitHub]

In-the-Wild Image Splice Dataset

[Download (89.2 MB)]

Paper


M. Huh, A. Liu, A. Owens, A. A. Efros,
Fighting Fake News: Image Splice
Detection via Learned Self-Consistency
In ECCV, 2018 (arXiv).
[Bibtex]


Popular Press

Acknowledgements

This work was supported, in part, by DARPA grant FA8750-16-C-0166 and UC Berkeley Center for Long-Term Cybersecurity. We thank Hany Farid and Shruti Agarwal for their advice, assistance, and inspiration in building this project, David Fouhey and Allan Jabri for helping with the editing, and Abhinav Gupta for letting us use his GPUs. Finally, we thank the many Reddit and Onion artists who unknowingly contributed to our dataset

This work was supported, in part, by DARPA grant FA8750-16-C-0166 and UC Berkeley Center for Long-Term Cybersecurity.

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