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AI Deepfake Detection System

DeepFake detector is an AI-based deepfake detection system designed to identify manipulated facial videos using deep learning. The system extracts representative video frames, preprocesses facial regions, and classifies videos as real or fake using a fine-tuned ConvNeXt-Tiny model. To improve transparency and trust, Grad-CAM visualizations highlight the regions that influenced the model's prediction, enabling users to understand why a video was classified as manipulated. The project focuses entirely on visual analysis without relying on audio or lip-sync features.

Deepfake Detection Dashboard

Real / Fake

Detection

Grad-CAM

Explainability

Video Frames

Input

Key highlights

  • Visual-only deepfake detection
  • ConvNeXt-Tiny transfer learning
  • Frame extraction using OpenCV
  • Video classification (Real/Fake)
  • Grad-CAM explainability
  • Automated preprocessing pipeline
  • Face-focused visual analysis
  • Model evaluation with standard metrics
  • GPU-accelerated inference
  • Research-oriented architecture

Technologies

PythonPyTorchConvNeXt-TinyOpenCVGrad-CAMNumPyPandasMatplotlib

Gallery

Video Upload Interface
Prediction Result
Grad-CAM Visualization

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