Abstract
Advances in Generative adversarial networks (GAN) have significantly improved the quality of synthetic facial images, posing threats to many vital areas. Thus, identifying whether a presented facial image is synthesized is of forensic importance. Our fundamental discovery is the lack of capillaries in the sclera of the GAN-generated faces, which is caused by the lack of physical/physiological constraints in the GAN model. Because there are more or fewer capillaries in people’s eyes, one can distinguish real faces from GAN-generated ones by carefully examining the sclera area. Following this idea, we first extract the sclera area from a probe image, then feed it into a residual attention network to distinguish GAN-generated faces from real ones. The proposed method is validated on the Flickr-Faces-HQ and StyleGAN2/StyleGAN3-generated face datasets. Experiments demonstrate that the capillary in the sclera is a very effective feature for identifying GAN-generated faces. Our code is available at: https://github.com/10961020/Deepfake-detector-based-on-blood-vessels.
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Acknowledgements
The work is supported by the network emergency management research special topic (no. WLYJGL2023ZD003), the Opening Project of Guangdong Province Key Laboratory of Information Security Technology (Grant No. 2020B1212060078).
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Zhang, T., Peng, A., Zeng, H. (2024). Ignored Details in Eyes: Exposing GAN-Generated Faces by Sclera. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14451. Springer, Singapore. https://doi.org/10.1007/978-981-99-8073-4_43
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