2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017)
Honolulu, Hawaii, USA
July 21, 2017 to July 26, 2017
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 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.
Face, Feature extraction, Streaming media, Transform coding, Cameras, Support vector machines, Lighting
P. Zhou, X. Han, V. I. Morariu and L. S. Davis, "Two-Stream Neural Networks for Tampered Face Detection," 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, Hawaii, USA, 2017, pp. 1831-1839.