Deepfake Video Analysis System
Detecting synthetic media with deep learning precision
Overview
A computer vision system designed to detect deepfake videos using deep learning techniques and frame-by-frame analysis.
Problem Statement
The proliferation of deepfake technology poses significant threats to media authenticity, requiring automated detection systems that can identify synthetic content at scale.
Technical Approach
Developed a deep learning pipeline using PyTorch for model training and inference. Used OpenCV for video processing and frame extraction. Implemented data augmentation with Albumentations to improve model robustness.
Key Features
- Frame-by-frame video analysis
- Deep neural network for fake detection
- Data augmentation pipeline
- Batch processing for large video datasets
- Confidence scoring for predictions
Challenges & Learnings
Handling the variety of deepfake generation methods required extensive data augmentation and careful model architecture design. Optimized inference speed for real-time detection feasibility.
Outcome
Created a functional deepfake detection system capable of processing video content and providing authenticity assessments with measurable accuracy metrics.