README.md

metadata

title: AI Image Detector
emoji: 🔍
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 6.0.2
app_file: app.py
pinned: false
license: mit

AI Image Detector

Detect whether an image is AI-generated or a real photograph using deep learning.

Model Architecture

• Base Model: MobileNetV2 (pretrained on ImageNet)
• Transfer Learning: Custom classification head with:
  • Global Average Pooling
  • Batch Normalization
  • Dense layer (256 units) with L2 regularization
  • Dropout (0.7)
  • Sigmoid output for binary classification

Training Data

The model was trained on a combined dataset of ~128,000 images:

• CIFAKE Dataset: 100,000 images (50k real, 50k AI-generated)
• Tiny GenImage Dataset: 28,000 additional images from various AI generators including:
  • Midjourney
  • Stable Diffusion
  • BigGAN
  • ADM
  • GLIDE
  • VQDM
  • Wukong

Usage

Upload any image to the interface, and the model will predict whether it's:

• Real Image: A genuine photograph
• AI-Generated: Created by an AI model

Files

• app.py - Main Gradio application
• requirements.txt - Python dependencies
• transfer_model.keras - Trained model weights

License

MIT License

Acknowledgments

• CIFAKE Dataset: https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images
• Tiny GenImage Dataset: https://www.kaggle.com/datasets/yangsangtai/tiny-genimage