Detecting Deepfakes: A Novel Framework Employing XceptionNet-Based Convolutional Neural Networks

Social networking sites have become primary sources of information for web users, making the rapid dissemination of deepfakes a cause for concern. Deepfakes are digitally manipulated images or videos that contain the computer-generated face of another person. Advancements in hardware and computational technologies have made the creation of deepfakes increasingly accessible, even to individuals without technical expertise. The potential harm posed by deepfakes necessitates urgent efforts to improve the detection of these manipulated media. Deep learning (DL) models have experienced rapid growth, enabling the synthesis and generation of hyper-realistic videos, often referred to as "deepfakes." DL algorithms can now create faces, swap faces between 2 individuals in video, and modify facial expressions, gender, also other features. These video manipulation techniques have applications in numerous fields, but deepfakes specifically exploit DL to synthesize and alter images in a manner that makes it difficult to discern between fake and genuine media. In this study, we present novel deepfake detection framework using DL and pre-trained XceptionNet model depends upon deep CNNs (Convolutional Neural Networks). We employ facial landmark recognition to extract information related to several facial characteristics from videos. This data is then used to facilitate the deep learning model's differentiation between genuine and deepfake videos. Features extracted from videos are utilized to train CNN concurrently. Our deepfake detection system is built on a multi-input Xception Neural Network model, which leverages CNNs. The system is trained using the Dessa Dataset and subset of Deepfake Detection Challenge Dataset. Proposed model demonstrates strong performance, achieving 96% classification accuracy and an AUC of 0.97, offering a promising solution for detecting deepfake videos.