2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIFS-1.2
Paper Title EXPOSING GAN-GENERATED FACES USING INCONSISTENT CORNEAL SPECULAR HIGHLIGHTS
Authors Shu Hu, University at Buffalo, State University of New York, United States; Yuezun Li, Ocean University of China, China; Siwei Lyu, University at Buffalo, State University of New York, United States
SessionIFS-1: Multimedia Forensics 1
LocationGather.Town
Session Time:Tuesday, 08 June, 13:00 - 13:45
Presentation Time:Tuesday, 08 June, 13:00 - 13:45
Presentation Poster
Topic Information Forensics and Security: [MMF] Multimedia Forensics
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Sophisticated generative adversary network (GAN) models are now able to synthesize highly realistic human faces that are difficult to discern from real ones visually. In this work, we show that GAN synthesized faces can be exposed with the inconsistent corneal specular highlights between two eyes. The inconsistency is caused by the lack of physical/physiological constraints in the GAN models. We show that such artifacts exist widely in high-quality GAN synthesized faces and further describe an automatic 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.