GAN-generated face detection by color feature and ResNet

Liang Zhang

doi:10.1117/12.3011989

7 December 2023 GAN-generated face detection by color feature and ResNet

Liang Zhang

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294147 (2023) https://doi.org/10.1117/12.3011989
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Deep generative models, particularly Generative Adversarial Networks (GANs), have the capability to generate highly realistic fake face images that can easily deceive humans. Consequently, the utilization of machine learning techniques to discern these fake faces is becoming increasingly crucial. In this study, we initially analyze the characteristics of face images generated by GANs, and subsequently propose the extraction of color features as the key discriminator between real and fake faces. Regarding the choice of classifier, this paper adopts ResNet for accurate detection. The experimental results indicate that the method presented in this paper exhibits superior efficiency in detecting fake faces.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liang Zhang "GAN-generated face detection by color feature and ResNet", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294147 (7 December 2023); https://doi.org/10.1117/12.3011989

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KEYWORDS

Facial recognition systems

Education and training

Feature extraction

Machine learning

Gallium nitride

Counterfeit detection

Cameras

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