Exposing AI Generated Deepfake Images Using Siamese Network with Triplet Loss - Kanwal, Sidra; Tehsin, Samabia; Saif, Shahela (Computing and Informatics, 2023)
Generative Adversarial Networks have gained popularity mainly due to their ability to create fake human faces. The remarkable detail with which such images have been created in the past few years has exceeded the ability of humans to differentiate between these fake images and real images. Such images have been known to be capable of deceiving the face recognition systems with certain success as well. Forensic systems being developed nowadays take into account adversarial attacks in order to create a more comprehensive detection approaches. Different GAN algorithms such as StackGAN, StyleGAN use different architectures to produce images. Since the underlying technique is different from one another it is difficult for any single detection algorithm trained on one kind of GAN to detect fake images generated from some other kind of GAN. In this research we use a siamese network with triplet loss function to provide a generic solution for detection of GAN generated images or deepfake images. Extensive experiments have been conducted to analyze the effectiveness of the proposed approach. The results show that the siamese triplet loss network performs significantly better than the contemporary approaches with accuracy exceeding 90 % in most experiments.