AI Video Reality Detector
A deep learning system designed to detect whether a video is AI-generated or real.
Project Overview
The AI Video Reality Detector is a computer vision and deep learning system that analyzes video content to determine whether it was generated by AI or recorded from real-world footage.
Problem Statement
AI-generated videos are increasingly realistic, making manual verification unreliable. There is a growing need for automated tools capable of detecting synthetic video content at scale.
Goals & Objectives
- Detect AI-generated vs real videos
- Build a scalable video analysis pipeline
- Maintain accuracy across varying video quality
Solution Design
The system processes videos through a structured pipeline involving frame extraction, preprocessing, feature analysis, and deep learning classification using pretrained models.
Technology Stack
- Python
- OpenCV for video processing
- HuggingFace pretrained models
Challenges & Solutions
- Dataset Variability: Solved through normalization and consistent preprocessing.
- Model Generalization: Improved using diverse datasets and evaluation across unseen samples.
- Computational Cost: Reduced using selective frame sampling.
Current Status
The project is currently in development, focusing on improving model accuracy and building a web-based interface for real-time detection.
Key Learnings
- Data quality has a major impact on model accuracy
- Deepfake detection is an evolving technical challenge
- Efficient pipelines are essential for video processing