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.

研究表明，在医学图像领域，使用综合评估 13 种先进的深度卷积神经网络（DCNNs）有效地区分真实的和篡改或操纵的医学图像。发现 ResNet50V2 在精确度和特异度方面表现出色，而 DenseNet169 在准确性、召回率和 F1 得分方面表现突出。同时，MobileNetV3Large 在保持相对较小的参数数量的同时，具有较高的性能。研究还发现 DenseNet 和 EfficientNet 模型族在隐空间分离质量上卓越，提供对医学图像造假的更深入理解。该研究对医学图像领域的深度伪造图像检测做出了有价值的贡献。