steganograph-ia-detector
Fine-tuned Vision Transformer (ViT) for AI-generated image detection.
This model is a binary classifier trained to distinguish real photographs from images generated by modern AI models (Stable Diffusion 2.1 / XL / 3, DALL-E 3, Midjourney v6).
Part of the SteganographIA project (MIAGE TPI).
Performance on the test set (15,000 balanced images)
| Metric | Value |
|---|---|
| Accuracy | 0.923 |
| Real precision / recall | 0.88 / 0.99 |
| AI precision / recall | 0.98 / 0.86 |
The model is conservative: it almost never accuses a real image of being AI-generated (1.4% false positive rate), but misses ~14% of AI images by classifying them as real.
Training details
- Base model:
google/vit-base-patch16-224 - Dataset:
Rajarshi-Roy-research/Defactify_Image_Dataset(Defactify Challenge @ AAAI), rebalanced 50/50 by undersampling the AI class stratified across the 5 generators. - Train / Val / Test split: 14,000 / 3,000 / 15,000 images.
- Optimizer: AdamW
- Learning rate: 2e-5
- Batch size: 16
- Epochs: 3
- Mixed precision (fp16) on a single Tesla T4 GPU.
- Training time: ~16 minutes.
How to use
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch
processor = AutoImageProcessor.from_pretrained("delpot/steganograph-ia-detector")
model = AutoModelForImageClassification.from_pretrained("delpot/steganograph-ia-detector")
image = Image.open("path/to/image.jpg").convert("RGB")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted = logits.argmax(-1).item()
print(model.config.id2label[predicted])
Limitations
- Trained on Defactify only. Generalization to other generators (notably Midjourney V1/V2, Flux, or future models not yet released) is not guaranteed.
- Lower recall on the AI class: ~14% of AI-generated images are classified as real. For stricter use cases (e.g. moderation of AI content), the decision threshold could be adjusted.
- Input images are resized to 224x224, so very high-resolution artifacts may be lost.