MedNet: Medical deepfakes detection using an improved deep learning approach

Recently, the massive development in the field of deep learning (DL) and artificial intelligence (AI)-aware tools have forced the requirement of caution when using several types of digital data. Serious security and privacy concerns have risen due to the significant advancements in the creation of the manipulated technique known as deepfakes. One avenue of deepfakes is to add and eliminate tumors from medical images. The inability of the automated systems to detect medical deepfakes can cause serious security and privacy problems resulting in an extensive burden on hospital assets or even loss of human life. To counter such effects a reliable deepfakes detector that can tackle the latest manipulation generation approaches is required. In the presented work, we attempt to solve the problem by introducing a DL method called the MedNet model to detect lung CT-Scan-based deepfakes samples. Descriptively, we have proposed a custom EfficientNetV2-B4 framework with extra added dense layers at the last of the network. To further increase the feature computation ability of the introduced approach, we proposed a spatial-channel attention mechanism to emphasize the altered areas of samples which result in improved classification performance. Extensive experimentation containing a standard dataset called the CT-GAN dataset is performed to show the efficiency of the presented work. We have attained an accuracy score of 85.49% which is showing the effectiveness of the presented work in reliably detecting the real samples of lung CT-Scan from the deepfake images.