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---
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 |