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license: mit
library_name: onnx
pipeline_tag: image-classification
tags:
  - image-classification
  - onnx
  - efficientnet
  - deepfake-detection
  - ai-generated-image-detection
  - cifake
  - transfer-learning
  - pytorch
  - binary-classification
datasets:
  - birdy654/cifake-real-and-ai-generated-synthetic-images
metrics:
  - accuracy
model-index:
  - name: CIFAKE-EfficientNet-B0
    results:
      - task:
          type: image-classification
          name: Binary Image Classification
        dataset:
          name: CIFAKE
          type: birdy654/cifake-real-and-ai-generated-synthetic-images
        metrics:
          - type: accuracy
            value: 97.85
            name: Test Accuracy

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

Phase	Epochs	Strategy	Learning Rate	Scheduler
1	10	Feature extraction (backbone frozen)	3e-4	—
2	5	Full fine-tuning (all layers)	3e-5	CosineAnnealingLR

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

Metric	Value
Test Accuracy	97.85%

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

Index	Class	Description
0	FAKE	AI-generated synthetic image
1	REAL	Real photograph (from CIFAR-10)

The model outputs a single logit. After applying sigmoid:

sigmoid(logit) >= 0.5 → REAL
sigmoid(logit) < 0.5 → FAKE

Dataset

The CIFAKE dataset consists of:

Split	Real	Fake	Total
Train	50,000	50,000	100,000
Test	10,000	10,000	20,000

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.