README.md

metadata

title: Testing AI Contain
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: docker
sdk_version: latest
app_file: app.py
pinned: false

Testing AI Contain

This Hugging Face Space uses Docker to run a custom environment for AI content detection.

How to run locally

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title: Testing AI Contain emoji: 🤖 colorFrom: blue colorTo: green sdk: docker sdk_version: "latest" app_file: app.py pinned: false

AI-Contain-Checker

AI-Content-Checker

A modular AI content detection system with support for image classification, image edit detection, Nepali text classification, and general text classification. Built for performance and extensibility, it is ideal for detecting AI-generated content in both visual and textual forms.

🌟 Features

🖼️ Image Classifier

• Purpose: Classifies whether an image is AI-generated or a real-life photo.
• Model: Fine-tuned InceptionV3 CNN.
• Dataset: Custom curated dataset with ~79,950 images for binary classification.
• Location: features/image_classifier
• Docs: docs/features/image_classifier.md

🖌️ Image Edit Detector

• Purpose: Detects image tampering or post-processing.

• Techniques Used:

  • Error Level Analysis (ELA): Visualizes compression artifacts.
  • Fast Fourier Transform (FFT): Detects unnatural frequency patterns.

• Location: features/image_edit_detector

• Docs:

  • ELA
  • FFT
  • Metadata Analysis
  • Backend Notes

📝 Nepali Text Classifier

• Purpose: Determines if Nepali text content is AI-generated or written by a human.
• Model: Based on XLMRClassifier fine-tuned on Nepali language data.
• Dataset: Scraped dataset of ~18,000 Nepali texts.
• Location: features/nepali_text_classifier
• Docs: docs/features/nepali_text_classifier.md

🌐 English Text Classifier

• Purpose: Detects if English text is AI-generated or human-written.

• Pipeline:

  • Uses GPT2 tokenizer for input preprocessing.
  • Custom binary classifier to differentiate between AI and human-written content.

• Location: features/text_classifier

• Docs: docs/features/text_classifier.md

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🗂️ Project Structure

AI-Checker/
│
├── app.py                  # Main FastAPI entry point
├── config.py               # Configuration settings
├── Dockerfile              # Docker build script
├── Procfile                # Deployment file for Heroku or similar
├── requirements.txt        # Python dependencies
├── README.md               # You are here 📘
│
├── features/               # Core detection modules
│   ├── image_classifier/
│   ├── image_edit_detector/
│   ├── nepali_text_classifier/
│   └── text_classifier/
│
├── docs/                   # Internal and API documentation
│   ├── api_endpoints.md
│   ├── deployment.md
│   ├── detector/
│   │   ├── ELA.md
│   │   ├── fft.md
│   │   ├── meta.md
│   │   └── note-for-backend.md
│   ├── functions.md
│   ├── nestjs_integration.md
│   ├── security.md
│   ├── setup.md
│   └── structure.md
│
├── IMG_Models/             # Saved image classifier model(s)
│   └── latest-my_cnn_model.h5
│
├── notebooks/              # Experimental and debug notebooks
├── static/                 # Static assets if needed
└── test.md                 # Test notes

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📚 Documentation Links

• API Endpoints

• Deployment Guide

• Detector Documentation

  • Error Level Analysis (ELA)
  • Fast Fourier Transform (FFT)
  • Metadata Analysis
  • Backend Notes

• Functions Overview

• NestJS Integration Guide

• Security Details

• Setup Instructions

• Project Structure

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🚀 Usage

1. Install dependencies

docker build -t testing-ai-contain .
docker run -p 7860:7860 testing-ai-contain

pip install -r requirements.txt

2. Run the API

   chroma run --path ./chroma_database ## to run chromadb locally
   uvicorn app:app --reload --port 8001 ## fastapi (run after chromadb)
   

3. Build Docker (optional)

   docker build -t ai-contain-checker .
   docker run -p 8000:8000 ai-contain-checker
   

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🔐 Security & Integration

• Token Authentication and IP Whitelisting supported.
• NestJS integration guide: docs/nestjs_integration.md
• Rate limiting handled using slowapi.

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🛡️ Future Plans

• Add video classifier module.
• Expand dataset for multilingual AI content detection.
• Add fine-tuning UI for models.

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📄 License

See full license terms here: LICENSE.md