{"repo":"ali-faraz-py/deepfake-detector","free":true,"listed":false,"github":"https://github.com/ali-faraz-py/deepfake-detector","clone":"git clone https://github.com/ali-faraz-py/deepfake-detector.git","description":"AI-powered deepfake detector that classifies uploaded face images and videos as Real or Fake with confidence scores. Full-stack app built with PyTorch (EfficientNet-B0), FastAPI, and Next.js — trained on 100k+ images and deployed with Vercel + Render.","language":"JavaScript","stars":11,"topics":["ai","computer-vision","cybersecurity","deepfake-detection","efficientnet","fastapi","full-stack","machine-learning","nextjs","pytorch"],"license":null,"category":"machine-learning","readme_excerpt":"🔍 Deepfake Detector An AI-powered web app that analyzes uploaded face images and videos to classify them as Real or Deepfake , with a confidence score. Built as a full-stack project covering model training, backend API development, and frontend deployment. Live app: deepfake-detector-olive.vercel.app Backend API: deepfake-detector-api-amqf.onrender.com --- Demo --- What it does - Upload a face image or video - The model classifies it as Real or Fake - Returns a confidence percentage, shown with a visual confidence bar - For videos, samples multiple frames and averages predictions across them - Drag-and-drop upload, live preview, and a history of recent checks - Try-it-now sample buttons (one real photo, one AI-generated photo) for visitors without their own test files - File type and size validation, with toast notifications for errors - Dark mode toggle - A visible disclaimer noting the model's known generalization limits (see below) --- Tech Stack Frontend - Next.js (React) - Tailwind CSS - Deployed on Vercel Backend - FastAPI (Python) - PyTorch + EfficientNet-B0 - OpenCV (video frame extraction) - Deployed on Render Model Training - Trained on Google Colab (free GPU) - Dataset: 140k Real and Fake Faces (Kaggle, by xhlulu) - 100,000 training images / 20,000 validation images - 3 epochs, 99.7% training accuracy --- How it works 1. The frontend sends the uploaded file to the FastAPI backend 2. If it's an image, it's resized, normalized, and passed through EfficientNet-B0 3. ","default_branch":null,"files":null,"tree":[],"storefront":"/r/ali-faraz-py","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ali-faraz-py/deepfake-detector/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}