Machine Learning based Medical Image Deepfake Detection: A Comparative Study
This addresses the critical issue of medical deepfake detection to prevent resource waste and loss of life, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of detecting deepfake medical images involving tumor injections and removals by evaluating eight machine learning algorithms, achieving near perfect accuracy in detection.
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.