A New Approach for Effective Medical Deepfake Detection in Medical Images

Abstract

In today's world, deepfake technology is being used to generate fake images, sounds, and videos from real images and sounds using deep learning and artificial intelligence techniques. It is possible to manipulate medical images with this technology. The manipulation of medical images can lead to incorrect diagnoses by medical professionals, disrupting the functioning of hospitals. As a result of these disruptions, hospitals may experience significant financial and life-threatening problems. In this study, it is aimed to obtain an effective deep learning-based method to detect manipulated medical images. Initially, two distinct datasets are created which contain Knee Osteoarthritis X-ray and lung CT scans. Data pre-processing and augmentation methods are applied for data standardization and variation. The instances in datasets are labeled as real or fake. The medical deepfake distinguish ability of YoloV3, YoloV5nu, YoloV5su, YoloV8n, YoloV8s, YoloV8m, YoloV8l, YoloV8x models tested on these datasets. In the analysis performed, all YOLO models showed almost full success in distinguishing Knee Osteoarthritis X-ray images. In lung CT scan images, although YoloV8 models generally achieved good performance, the YoloV5 models gave the best and worst results. While the best result was obtained from YoloV5su with a recall value of 0.997, the worst result was obtained from the YoloV5nu model with a recall value of 0.91. Furthermore, the best model (YoloV5su) works 60% faster than YoloV8x model, which has the second highest performance. The findings show that YoloV5su can be used for fast and accurate medical deepfake detection.