Deepfake Video Detection Using Convolutional Neural Network Based Hybrid Approach

Given the rapid advancement of deepfake technology, which allows for the creation of highly realistic fake content, there is a pressing need for an efficient solution to address the security risks associated with this technology. Deepfake videos are widely recognized for their significant implications, including the potential for identity theft, the dissemination of false information, and the endangerment of national security. Therefore, it is crucial to develop and enhance the reliability of deepfake detection algorithms. In this study, feature extraction techniques were performed to utilize deep learning algorithms such as Xception and ResNet50 to detect deepfakes in a video dataset using the DFDC dataset. Additionally, a total of eight hybrid models were developed using various classification algorithms such as SVM, KNN, MLP, and RF. The ResNet50 and RF hybrid models achieved the highest accuracy rate of 98%, with an AUC value of 99.65%. This study presents a machine learning method that has been developed to address different technical challenges in the field of deepfake detection and effectively identify deepfakes. The proposed method has demonstrated successful performance compared to state-of-the-art models, proving its effectiveness in accurately detecting fake content within videos.