  Generative Adversarial Networks (GANs) have exhibited noteworthy advancements
across various applications, including medical imaging. While numerous
state-of-the-art Deep Convolutional Neural Network (DCNN) architectures are
renowned for their proficient feature extraction, this paper investigates their
efficacy in the context of medical image deepfake detection. The primary
objective is to effectively distinguish real from tampered or manipulated
medical images by employing a comprehensive evaluation of 13 state-of-the-art
DCNNs. Performance is assessed across diverse evaluation metrics, encompassing
considerations of time efficiency and computational resource requirements. Our
findings reveal that ResNet50V2 excels in precision and specificity, whereas
DenseNet169 is distinguished by its accuracy, recall, and F1-score. We
investigate the specific scenarios in which one model would be more favorable
than another. Additionally, MobileNetV3Large offers competitive performance,
emerging as the swiftest among the considered DCNN models while maintaining a
relatively small parameter count. We also assess the latent space separability
quality across the examined DCNNs, showing superiority in both the DenseNet and
EfficientNet model families and entailing a higher understanding of medical
image deepfakes. The experimental analysis in this research contributes
valuable insights to the field of deepfake image detection in the medical
imaging domain.
