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Two-Stream Neural Networks for Tampered Face Detection
AbstractWe propose a two-stream network for face tampering detection. We train GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stream. In addition, we use two different online face swaping applications to create a new dataset that consists of 2010 tampered images, each of which contains a tampered face. We evaluate the proposed two-stream network on our newly collected dataset. Experimental results demonstrate the effectness of our method.
Related Material
[pdf][bibtex]@InProceedings{Davis_2017_CVPR_Workshops,
author = {Zhou; Xintong Han; Vlad Morariu; Larry Davis, Peng I. S.},
title = {Two-Stream Neural Networks for Tampered Face Detection},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}
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