Minyoung Huh and Andrew Liu and Andrew Owens and Alexei (Alyosha) Efros

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2018-67

May 14, 2018

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-67.pdf

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 training data. In this paper, we propose a model that learns to detect visual manipulations from unlabeled data through self-supervision. Given a large collection of real photographs with automatically recorded EXIF metadata, we train 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-supervised learning method to the task of detecting and localizing image splices. Although the proposed model obtains state-of-the-art performance on several benchmarks, we see it as merely a step in the long quest for a truly general-purpose visual forensics tool.

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BibTeX citation:

@techreport{Huh:EECS-2018-67,
    Author= {Huh, Minyoung and Liu, Andrew and Owens, Andrew and Efros, Alexei (Alyosha)},
    Title= {Fighting Fake News: Image Splice Detection via Learned Self-Consistency},
    Year= {2018},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-67.html},
    Number= {UCB/EECS-2018-67},
    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 training data. In this paper, we propose a model that learns to detect visual manipulations from unlabeled data through self-supervision. Given a large collection of real photographs with automatically recorded EXIF metadata, we train 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-supervised learning method to the task of detecting and localizing image splices. Although the proposed model obtains state-of-the-art performance on several benchmarks, we see it as merely a step in the long quest for a truly general-purpose visual forensics tool.},
}

EndNote citation:

%0 Report
%A Huh, Minyoung 
%A Liu, Andrew 
%A Owens, Andrew 
%A Efros, Alexei (Alyosha) 
%T Fighting Fake News: Image Splice Detection via Learned Self-Consistency
%I EECS Department, University of California, Berkeley
%D 2018
%8 May 14
%@ UCB/EECS-2018-67
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-67.html
%F Huh:EECS-2018-67