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We propose a method for detecting face swapping and other identitymanipulations in single images. Face swapping methods, such as DeepFake,manipulate the face region, aiming to adjust the face to the appearance of itscontext, while leaving the context unchanged. We show that this modus operandiproduces discrepancies between the two regions. These discrepancies offerexploitable telltale signs of manipulation. Our approach involves two networks:(i) a face identification network that considers the face region bounded by atight semantic segmentation, and (ii) a context recognition network thatconsiders the face context (e.g., hair, ears, neck). We describe a method whichuses the recognition signals from our two networks to detect suchdiscrepancies, providing a complementary detection signal that improvesconventional 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 evengeneralizes to detect fakes produced by unseen methods.

DeepFake Detection Based on the Discrepancy Between the Face and its Context