Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights

by   Shu Hu, et al.

Sophisticated generative adversary network (GAN) models are now able to synthesize highly realistic human faces that are difficult to discern from real ones visually. GAN synthesized faces have become a new form of online disinformation. In this work, we show that GAN synthesized faces can be exposed with the inconsistent corneal specular highlights between two eyes. We show that such artifacts exist widely and further describe a method to extract and compare corneal specular highlights from two eyes. Qualitative and quantitative evaluations of our method suggest its simplicity and effectiveness in distinguishing GAN synthesized faces.


page 1

page 2

page 3

page 4

page 6


Exposing GAN-synthesized Faces Using Landmark Locations

Generative adversary networks (GANs) have recently led to highly realist...

Stereotype-Free Classification of Fictitious Faces

Equal Opportunity and Fairness are receiving increasing attention in art...

Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces

Deepfake represents a category of face-swapping attacks that leverage ma...

Fighting deepfakes by detecting GAN DCT anomalies

Synthetic multimedia content created through AI technologies, such as Ge...

TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing

Generative Adversarial Networks (GAN)-synthesized table publishing lets ...

ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade Forests for Detecting GAN-generated Faces

The prominent progress in generative models has significantly improved t...

Please sign up or login with your details

Forgot password? Click here to reset