Comparative Analysis of Deep Convolutional Neural Networks for Detecting Medical Image Deepfakes

TL;DR

This study evaluates 13 deep convolutional neural networks for detecting deepfakes in medical images, highlighting their strengths in accuracy, speed, and resource efficiency, and providing insights into their suitability for different scenarios.

Contribution

It offers a comprehensive comparison of various DCNN architectures for medical image deepfake detection, including performance metrics and scenario-based recommendations.

Findings

DenseNet169 achieves the highest accuracy, recall, and F1-score.

ResNet50V2 demonstrates superior precision and specificity.

MobileNetV3Large is the fastest model with a small parameter count.

Abstract

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.