We 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 swapping 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 effectiveness of our method.
@article{arxiv.1803.11276,
title = {Two-Stream Neural Networks for Tampered Face Detection},
author = {Peng Zhou and Xintong Han and Vlad I. Morariu and Larry S. Davis},
journal= {arXiv preprint arXiv:1803.11276},
year = {2018}
}