Smart Deepfake Detection System

Deepfakes are artificially generated multimedia content that can convincingly mimic real human faces and voices using advanced AI techniques such as Generative Adversarial Networks (GANs). This poses serious ethical, social, and security challenges in digital communication. To address this issue, the proposed project presents a Multimodal Deepfake Detection System that integrates image, video, and audio analysis pipelines within a unified framework. The system employs Efficient Net-based CNN for image forgery detection, CNN combined with Bi-LSTM for temporal video analysis, and a 1D-CNN with LSTM for detecting manipulated or cloned audio. The predictions from these modalities are combined using a Fuzzy Fusion Engine, which intelligently weights each confidence score to produce a final verdict with high accuracy and interpretability. The model is trained using public Deepfake datasets such as Face Forensics++, Celeb-DF, and DFDC, with binary cross-entropy loss, data augmentation, and early stopping to ensure stable convergence and better generalization. The trained models are deployed on Hugging Face Spaces, while the web interface is hosted on Vercel, enabling real-time Deepfake detection for users through a browser interface. This approach enhances detection accuracy by leveraging multimodal evidence (visual, temporal, and auditory), improves generalization across datasets, and provides an explainable and efficient solution to combat the growing threat of Deepfakes.