DeepFake Image Detection Using Adaptive Discriminator Augmentation (ADA)

The proliferation of Deepfake technology, driven by artificial intelligence (AI), poses a growing threat as it facilitates the dissemination of hate speech and misinformation through convincingly fabricated images. Deepfakes, a subdomain of AI, manipulate and superimpose one person's face onto another's, exploiting the power of machine learning to create deceptive content at an unprecedented pace and affordability. Despite the controversial nature of Deepfakes, their use is expanding both commercially and collectively. This paper offers an in-depth investigation into the effectiveness of StyleGAN2-ADA in identifying fake images, with a particular focus on detecting Deepfakes. We propose a novel GAN Discriminator model designed to enhance the accuracy of this detection process. Our model's training dataset comprises an extensive collection of 76,400 images from the FFHQ dataset. In our experimental evaluation, we subjected our model to 100 fake images, achieving an impressive detection rate of 95.71 %. This remarkable outcome underscores the efficacy of our approach in identifying Deepfakes. Furthermore, our technique demonstrates exceptional precision in distinguishing between real and fake images, promising a robust defense against the harmful impact of AI-generated fake content.