  We propose a method for detecting face swapping and other identity
manipulations in single images. Face swapping methods, such as DeepFake,
manipulate the face region, aiming to adjust the face to the appearance of its
context, while leaving the context unchanged. We show that this modus operandi
produces discrepancies between the two regions. These discrepancies offer
exploitable telltale signs of manipulation. Our approach involves two networks:
(i) a face identification network that considers the face region bounded by a
tight semantic segmentation, and (ii) a context recognition network that
considers the face context (e.g., hair, ears, neck). We describe a method which
uses the recognition signals from our two networks to detect such
discrepancies, providing a complementary detection signal that improves
conventional real vs. fake classifiers commonly used for detecting fake images.
Our method achieves state of the art results on the FaceForensics++,
Celeb-DF-v2, and DFDC benchmarks for face manipulation detection, and even
generalizes to detect fakes produced by unseen methods.
