README.md

metadata

title: ImgAuth AI
emoji: 🛡️
colorFrom: purple
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false

ImgAuth AI — Image Authenticity Detector

🛡️ ImgAuth AI is a state-of-the-art web application designed to detect AI-generated and manipulated images. Built with a "simple on the surface, powerful underneath" philosophy, it combines deep learning models with advanced digital forensics heuristics to deliver clear, binary verdicts: Likely AI-Generated or Likely Authentic.

Developed as a Major Project by Team VisionGuard (student team of 4).

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🚀 Key Features

• Binary Classification: Simplified verdicts removing ambiguity ("Likely AI-Generated" or "Likely Authentic").
• Deep Learning Ensemble: Combined predictions from 3 Hugging Face model pipelines:
  • umm-maybe/AI-image-detector
  • dima806/ai_vs_real_image_detection
  • Organika/sdxl-detector
• 5 Forensic Heuristics: Multi-layer analysis for technical validation:
  1. Noise Kurtosis Analysis (checks high-frequency noise distributions)
  2. Deep Feature Inconsistency (DFI) (checks patch-level consistency of Vision Transformer embeddings)
  3. FFT Spectral Analysis (identifies periodic artifacts in frequency domain)
  4. Color Histogram Analysis (detects synthetic pixel roughness/smoothness)
  5. JPEG Ghost Analysis (detects double compression artifacts in JPEG files)
• AI Focus Areas (Explainability): Visual heatmaps showing ViT Attention Maps and Deep Feature Inconsistencies.
• Collapsible Technical Drawer: Advanced forensic signal logs, weights, and metrics available for researchers, while maintaining a clean, technical-jargon-free interface for everyday users.
• Privacy First: Fully stateless architecture; no images are stored permanently. Scanning history is saved only in local browser storage (localStorage).

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👥 Meet Team VisionGuard

• Vishal Chauhan (Computer Science & Engineering, Project Lead)
• Prince Mishra (Computer Science & Engineering, Backend Developer)
• Prince Dubey (Computer Science & Engineering, Security & Testing)
• Raksha (Computer Science & Engineering, Frontend Developer)

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🛠️ Technology Stack

• Backend: FastAPI, Uvicorn, PyTorch, Hugging Face Transformers, OpenCV, NumPy, SciPy
• Frontend: Vanilla HTML5, CSS3 (Modern dark-theme layout with purple gradients & glassmorphism), Vanilla JavaScript
• Deployment: Docker, Hugging Face Spaces

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💻 Local Setup and Running

To run this application locally on your machine, follow these steps:

Prerequisites

• Python 3.10+
• Pip package manager

Installation

1. Clone the repository:

   git clone <repository-url>
   cd imgauth-ai
   

2. Create and activate a virtual environment:

   • Windows (PowerShell):

     python -m venv .venv
     .\.venv\Scripts\activate
     

   • macOS/Linux:

     python -m venv .venv
     source .venv/bin/activate
     

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run the server:

   python run.py
   

   The app will start running at: http://localhost:5000

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🐳 Running with Docker

Alternatively, build and run via Docker:

1. Build the image:

   docker build -t imgauth-ai .
   

2. Run the container:

   docker run -p 7860:7860 imgauth-ai
   

   Open browser to: http://localhost:7860

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⚖️ License & Attribution

• Non-Commercial: This project uses the Organika/sdxl-detector model, licensed under CC BY-NC 4.0. It is intended strictly for non-commercial educational and research purposes.
• Model Attribution: All deep learning classifications are handled by model weights published by the Hugging Face community.