  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.
