Machine Learning based Medical Image Deepfake Detection: A Comparative Study

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. We evaluate different machine learning algorithms and pretrained convolutional neural networks on distinguishing between tampered and untampered data. The findings of this work show near perfect accuracy in detecting instances of tumor injections and removals.