Deepfake Detection System Development

AI-Based Deepfake Detection System (Image + Video + Audio)

I am looking for an experienced developer or AI/ML engineer to help build or enhance a complete deepfake detection system capable of identifying whether content is real or AI-generated.

Project Overview:
The system should analyze multiple content types:
• Images (detect facial artifacts and inconsistencies)
• Videos (frame-level analysis + temporal inconsistency detection)
• Audio (spectral patterns, voice anomalies, speaker embeddings)

Core Requirements:
• Multi-modal deepfake detection (Image, Video, Audio)
• Clean and scalable Python-based architecture
• Support for datasets like FaceForensics++ and DFDC
• Training, evaluation, and inference pipeline
• Option to run via CLI, Jupyter Notebook, or executable (.exe)

Tech Stack (Preferred):
Python, OpenCV, TensorFlow/PyTorch, scikit-learn
Audio processing tools (LibROSA, etc.)
Frontend + Backend integration (optional but preferred)

Additional Features (Bonus):
• Web interface (FastAPI + frontend)
• Runtime learning / feedback system
• Lightweight dataset crawler
• Performance optimization for low-resource systems

Deliverables:
• Fully working deepfake detection pipeline
• Clean, modular code with proper documentation
• Setup instructions and dependency guide
• Testing and evaluation results
• Optional: Deployable full-stack system

Notes:
• The solution should be scalable and easy to maintain
• Preference for clean UI and efficient performance
• Open to suggestions and improvements

If you have experience in AI/ML, computer vision, or deep learning projects, feel free to apply with your approach and past work.

Looking forward to collaborating!