cftcimert/cifake-efficientnet-b0
1
CIFAKE — Real vs AI-Generated Image Classifier
A binary image classifier that distinguishes real photographs from AI-generated (synthetic) images, built with EfficientNet-B0 and transfer learning.
Model Description
This model was trained to detect AI-generated images using the CIFAKE dataset, which contains 120,000 images (60K real from CIFAR-10 + 60K synthetic from Stable Diffusion v1.4).
- Architecture: EfficientNet-B0 (pretrained on ImageNet)
- Task: Binary classification (Real vs Fake)
- Output: Single logit → apply sigmoid to get P(Real)
- Format: ONNX (opset 17)
- Input: RGB image tensor of shape
[batch, 3, 224, 224], normalized with ImageNet statistics
Training Details
Two-Phase Training Strategy
- Optimizer: AdamW (weight_decay=1e-2)
- Loss: BCEWithLogitsLoss
- Batch Size: 128
- Mixed Precision: FP16 via
torch.autocast
Preprocessing
transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC, antialias=True)
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])Performance
Evaluated on 20,000 test images (10K real + 10K AI-generated).
Usage
Python (ONNX Runtime)
import numpy as np
import onnxruntime as ort
from PIL import Image
from torchvision import transforms
from torchvision.transforms import InterpolationMode
# Load model
session = ort.InferenceSession("model.onnx")
# Preprocess image
transform = transforms.Compose([
transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC, antialias=True),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image = Image.open("your_image.jpg").convert("RGB")
input_tensor = transform(image).unsqueeze(0).numpy()
# Run inference
logit = session.run(None, {"input": input_tensor})[0]
probability = 1 / (1 + np.exp(-logit)) # sigmoid
label = "REAL" if probability >= 0.5 else "FAKE"
confidence = float(probability) if label == "REAL" else float(1 - probability)
print(f"{label} ({confidence * 100:.2f}% confidence)")Labels
The model outputs a single logit. After applying sigmoid:
sigmoid(logit) >= 0.5→ REALsigmoid(logit) < 0.5→ FAKE
Dataset
The CIFAKE dataset consists of:
Citation
@article{bird2024cifake,
title={CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images},
author={Bird, Jordan J and Lotfi, Ahmad},
journal={IEEE Access},
year={2024},
doi={10.1109/ACCESS.2024.3356122}
}License
This model is released under the MIT License.