DeepGuard: A Robust Multimodal Framework for Automated Deepfake Screening and Triage

Abstract

Deepfakes are synthetic images, videos, and voice recordings produced by deep generative models, and their realism now exceeds what human observers can reliably judge. Most existing detectors address a single medium, which leaves them blind to forgeries that live in motion or in sound. This paper presents DeepGuard, a unified multimodal framework that accepts an image, a video, or an audio file through one web interface and returns a Real or Deepfake verdict together with a confidence percentage. Uploaded media are normalized and, for video, separated into frames and an audio track. YOLOv8-Face localizes and crops the facial region, a ResNet-50 convolutional network extracts spatial forgery cues such as texture irregularities and blending artifacts, a bidirectional long short-term memory network models frame-to-frame inconsistencies, and a Wav2Vec2 model examines the speech signal for synthetic or cloned voices. A Bayesian weighted fusion stage combines whichever modality predictions are available into one deepfake probability, which is thresholded and stored with its metadata in a history database. The system is implemented with a Flask backend, OpenCV, and NumPy. The paper details the design rationale, the algorithm of each module, the fusion rule, and a leakage-free evaluation protocol based on accuracy, precision, recall, F1 score, and a confusion matrix. Quantitative results are intentionally not reported here; they will be added after experiments on benchmark datasets.