Today I started developing a deepfake detection system focused on creating an intelligent AI agent that can analyze videos for authenticity. I spent approximately 6.5 hours on the initial foundation work across several key areas.
Phase 1 (1.5 hours): Project Setup & Research - Established the project structure with proper Python environment, installed core dependencies like PyTorch and FastAPI, and researched existing deepfake detection approaches. Created initial configuration files and set up version control with organized folder structure for models, data, and API components.
Phase 2 (2 hours): Core Architecture Design - Designed the concept of specialist models for different types of deepfake detection (background inconsistencies, compression artifacts, audio-visual sync issues). Created the basic model architecture using EfficientNet as backbone and planned the data pipeline for video processing with frame extraction and face detection capabilities.
Phase 3 (1.5 hours): First Model Implementation - Built the baseline generalist (BG) model with proper PyTorch implementation including training loops, loss functions, and checkpoint saving. Set up data loaders for the FaceForensics++ dataset and implemented basic video preprocessing pipeline with OpenCV for frame extraction and face cropping.
Phase 4 (1 hour): Basic Web Interface - Created a simple React frontend with video upload functionality and built corresponding FastAPI backend with prediction endpoints. Implemented basic video processing workflow that accepts uploaded videos, processes them through the model, and returns prediction results with confidence scores.
Phase 5 (30 minutes): Initial Testing & Documentation - Created a small test dataset with real and fake videos to validate the system works end-to-end. Set up basic evaluation metrics and testing scripts. Started documenting the architecture and created a development roadmap for the remaining specialist models and advanced features.
Key achievements include establishing a solid foundation with one working deepfake detection model achieving around 60% accuracy on initial tests, a functional web interface for testing, and a clear plan for expanding to multiple specialist models. The system successfully processes uploaded videos, extracts faces, runs inference, and displays results through a clean web interface.
The project demonstrates early-stage machine learning system development with proper software engineering practices including modular code organization, environment management, and basic testing infrastructure. Next phases will focus on implementing the remaining 5 specialist models and building the intelligent agent system for routing between them.
Screenshots to attach:
- Project Structure - VS Code showing organized folders (src/, models/, api/, data/)
- Training Progress - Terminal output showing model training with loss/accuracy metrics
- Web Interface - Simple upload page with video selection and results display
- Model Code - Screenshot of the BG model architecture implementation
- Test Results - Console showing initial predictions on sample videos
Total initial development time: 6.5 hours of focused development work.