---
license: gpl-3.0
pipeline_tag: image-classification
tags:
- ai-detection
- deepfake-detection
- image-classification
- computer-vision
- pytorch
---

# BAILU - Lightweight AI-Generated Image Detector

BAILU is a highly efficient deepfake detection model designed to identify AI-generated images from various image generation models. With only **2M parameters (~8MB)**, it achieves **95.88% overall accuracy** by analyzing artifacts/signatures unique to AI generation pipelines.

## 🌍 Why Open-Source Matters for Deepfake Detection

This model was only possible because companies like Black Forest Labs and Stability AI release their models publicly. Private, closed-source models create detection blind spots—we cannot defend against what we cannot study.
We strongly encourage all AI companies to open-source their models to enable:

- Effective deepfake detection research
- Transparency in AI development
- Collaborative safety measures
- Public trust through verifiable defenses

## 🎯 Key Features

- **Ultra-Lightweight**: 2M parameters, ~8MB model size - runs on CPU or GPU
- **Multi-VAE Detection**: Trained to detect artifacts from FLUX.1, FLUX.2, SDXL, and Stable Diffusion 1.5
- **High Accuracy**: 95.88% overall accuracy (97.75% AI detection rate, 94.00% real detection)
- **Fast Inference**: <10ms per image on modern GPUs
- **Open-Source Advocacy**: Built to demonstrate the importance of open-source model transparency

## 📊 Performance Metrics

| Metric | Score |
|--------|-------|
| **Overall Validation Accuracy** | 95.88% (767/800) |
| **Loss** | 0.2645 |

*Tested on balanced dataset of 400 AI-generated and 400 real images*

## 🎓 Training Details

- **Hardware**: NVIDIA RTX 5090
- **Training Time**: ~110 hours
- **Data Augmentation**: Random crops, flips, compression, resizing
- **Optimizer**: AdamW (lr=1e-4, weight_decay=1e-4)
- **Scheduler**: CosineAnnealingLR (T_max=50)
- **Loss**: Binary Cross-Entropy with Logits

Detection must keep pace with generation. That requires open access.
## ⚠️ Important Limitations

- Not foolproof: Adversarial attacks and new model architectures may evade detection (**We plan to train model capable of detecting adversarial attacks later.**)
- No attribution: Cannot identify which specific AI model created an image
- Temporal degradation: Effectiveness may decrease as new AI models emerge

Disclaimer: This tool is for research and educational purposes. Results should not be used as sole evidence in legal or high-stakes decisions without human expert verification.