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We propose a two-stream network for face tampering detection. We trainGoogLeNet to detect tampering artifacts in a face classification stream, andtrain a patch based triplet network to leverage features capturing local noiseresiduals and camera characteristics as a second stream. In addition, we usetwo different online face swapping applications to create a new dataset thatconsists of 2010 tampered images, each of which contains a tampered face. Weevaluate the proposed two-stream network on our newly collected dataset.Experimental results demonstrate the effectiveness of our method.

Two-Stream Neural Networks for Tampered Face Detection