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!