Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 44, Issue 10, Pages 6111-6121Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2021.3093446
Keywords
Faces; Videos; Information integrity; Benchmark testing; Training; Neck; Hair; Image forensics; deep learning; deep fake; face swapping; fake image detection
Funding
- European Research Council (ERC) through the European Unions Horizon 2020 research and innovation programme [ERC CoG 725974]
Ask authors/readers for more resources
The proposed method utilizes two networks to detect face swapping and other identity manipulations in single images, achieving state of the art results in detection accuracy.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available