Machine Learning based Medical Image Deepfake Detection: A Comparative Study

TL;DR

This study evaluates eight machine learning algorithms, including deep learning models, for detecting medical image deepfakes involving tumor injections or removals, achieving near-perfect accuracy.

Contribution

It provides a comprehensive comparison of traditional and deep learning methods for medical deepfake detection, highlighting the effectiveness of fine-tuned models.

Findings

Near-perfect accuracy in detecting tumor injections and removals

Deep learning models outperform conventional machine learning methods

Fine-tuning pre-trained models enhances detection performance

Abstract

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which including three conventional machine learning methods, support vector machine, random forest, decision tree, and five deep learning models, DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19, on distinguishing between tampered and untampered images.For deep learning models, the five models are used for feature extraction, then fine-tune for each pre-trained model is performed. The findings of this work show near perfect accuracy in detecting instances of tumor injections and removals.