Enhanced MesoNet ‐based deepfake detection using deep learning: A robust framework for multimedia forensics

Abstract The rapid advancement of deepfake technology poses a significant threat to digital content authenticity and public trust. Deepfakes leverage artificial intelligence to generate realistic yet manipulated images and videos, often for deceptive purposes. This study introduced an enhanced version of the MesoNet convolutional neural network tailored for deepfake detection. The model incorporates two additional convolutional layers, resulting in substantial performance gains across various metrics. It achieved a precision of 96.60%, recall of 95.33%, F 1 ‐score of 95.96%, accuracy of 95.59%, and a Matthews Correlation Coefficient (MCC) of 91.11%, outperforming baseline models such as ResNet‐50, VGG variants, and AlexNet. Additionally, a real‐time detection system was developed using a React frontend and Flask backend, demonstrating the model's potential for practical deployment. This research contributed a robust and scalable approach to deepfake detection and lays the groundwork for real‐world applications in digital forensics and content authenticity verification.