DeepFake Detection Based on Discrepancies Between Faces and Their Context

Yuval Nirkin, Lior Wolf, Yosi Keller, Tal Hassner

Research output: Contribution to journalArticlepeer-review

80 Scopus citations


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 (e.g., Fig. 1). 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 versus fake classifiers commonly used for detecting fake images. Our method achieves state of the art results on the FaceForensics++ and Celeb-DF-v2 benchmarks for face manipulation detection, and even generalizes to detect fakes produced by unseen methods.

Original languageEnglish
Pages (from-to)6111-6121
Number of pages11
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number10
StatePublished - 1 Oct 2022

Bibliographical note

Publisher Copyright:
© 1979-2012 IEEE.


  • Image forensics
  • deep fake
  • deep learning
  • face swapping
  • fake image detection


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