Comparative Analysis of Deepfake Detection Models on Diverse GAN-Generated Images

Abstract

Advancement in Artificial intelligence has resulted in evolvement of various Deepfake generation methods. This subsequently leads to spread of fake information which needs to be restricted. Deepfake detection methods offer solution to this problem. However, a particular Deepfake detection method which gives best results for a set of Deepfake images (generated by a particular generation method) fails to detect another set of Deepfake images (generated by another method). In this work various Deepfake detection methods were tested for their suitability to decipher Deepfake images generated by various generation methods. We have used VGG16, ResNet50, VGG19, and MobileNetV2 for deepfake detection and pre-trained models of StyleGAN2, StyleGAN3, and ProGAN for fake generation. The training dataset comprised of 200000 images, 50 % of which were real and 50% were fake. The best performing Deepfake detection model was VGG19 with more than 96 percent accuracy for StyleGAN2, StyleGAN3, and ProGAN-generated fakes.