Real-Time Deepfake Detection via Frame-Level EfficientNet Ensemble and Client-Server Deployment

DeepFake technology threatens the integrity of digital content by allowing the generation of hyperrealistic but misleading content. In this paper, we propose a frame-based DeepFake detection framework that aims to enhance detection accuracy and computational efficiency. Our approach takes advantage of three large-scale datasets—Celeb-DF, FaceForensics++ (FF++), and DeeperForensics—consisting of close to 17GB of training data. Preprocessing includes frame extraction and face alignment with Dlib’s 68-point facial landmark detection to maintain high-resolution facial features. Two CNN models, EfficientNet-B4 and EfficientNet-B5, are ensembled and trained to detect coarse and fine-grained forgery artifacts. The model performs AUC of 0.9958 and F1-score of 0.9726 on benchmark sets, on par with state-of-the-art techniques. To ensure real-world deployability, the detection pipeline is incorporated within a cross-platform client-server setup in a modular structure. A responsive web frontend and an Android app provide real-time predictions and Grad-CAM-based visualizations. This end-to-end solution bridges the gap between research and deployment by providing a scalable, interpretable, and accessible platform for DeepFake detection. Unlike previous methods, our system integrates multimodal detection in videos with a real-time, cross-platform deployment pipeline, bridging research with practical application.