You will get GenReAI – AI vs Real Face Detection Web App (Deep Learning + Flask)
Project details
GenReAI is a full-stack AI-powered web application designed to detect whether a face image is real or AI-generated using advanced deep learning models such as EfficientNetV2/XceptionNet. The system integrates a trained CNN model into a secure Flask-based backend with MongoDB Atlas for cloud database management.
The application enables users to securely register and log in, upload face images, and receive real-time classification results with confidence scores and probability breakdown. Each analysis is stored in the database, allowing users to access their prediction history through a dashboard interface.
The platform features a responsive, modern dark-themed UI with AJAX-based asynchronous processing for seamless user experience. Security measures include password hashing, protected routes, file validation, and session management.
This project demonstrates expertise in Artificial Intelligence, Computer Vision, full-stack web development, REST API architecture, and cloud database integration. It is suitable for use cases such as deepfake detection, digital identity verification, AI-generated media filtering, and cybersecurity applications.
The application enables users to securely register and log in, upload face images, and receive real-time classification results with confidence scores and probability breakdown. Each analysis is stored in the database, allowing users to access their prediction history through a dashboard interface.
The platform features a responsive, modern dark-themed UI with AJAX-based asynchronous processing for seamless user experience. Security measures include password hashing, protected routes, file validation, and session management.
This project demonstrates expertise in Artificial Intelligence, Computer Vision, full-stack web development, REST API architecture, and cloud database integration. It is suitable for use cases such as deepfake detection, digital identity verification, AI-generated media filtering, and cybersecurity applications.
Machine Learning Tools
ChatGPT, Deeplearning4j, GitHub Copilot, Google Data Studio, GPT-3, Keras, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learnWhat's included $500
These options are included with the project scope.
$500
- Delivery Time 5 days
- Number of Revisions 0
- Number of Model Variations 3
- Number of Scenarios 2
- Number of Graphs/Charts 2
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
Optional add-ons
You can add these on the next page.
Fast 4 Days Delivery
+$500About Tushar
Pen test, Python, MYSQL, ML, AI
Bhandara, India - 4:17 pm local time
Steps for completing your project
After purchasing the project, send requirements so Tushar can start the project.
Delivery time starts when Tushar receives requirements from you.
Tushar works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis & Planning
Understand detection objectives, model specifications, and system workflow.
Deep Learning Model Integration
Integrate and optimize the EfficientNetV2/XceptionNet model for AI vs real face classification.