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Creators/Authors contains: "Hu, Shu"

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  1. ; (, Journal of machine learning research)
    Sontag, David (Ed.)
    Accepted Manuscript Full Text Available
  2. ; (, Journal of machine learning research)
    Accepted Manuscript Full Text Available
  3. ; ; ; (, IEEE)
    Full Text Available 
  4. ; ; ; ; (, IEEE)
    Full Text Available 
  5. ; ; ; ; ; (, IEEE)
    Full Text Available 
  6. ; ; ; ; ; ; ; (, IEEE)
    Full Text Available 
  7. ; (, Proceedings of the 2nd Machine Learning for Health symposium)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; (, International Conference on Uncertainty in Artificial Intelligence (UAI))
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; (, Uncertainty in artificial intelligence)
    Accepted Manuscript Full Text Available
  10. ; ; (, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
    null (Ed.)
    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. 
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