👁 2

⚠️ Model Deprecated: model component is no longer recommended for use because it's outdated

open-deepfake-detection

open-deepfake-detection is a vision-language encoder model fine-tuned from siglip2-base-patch16-512 for binary image classification. It is trained to detect whether an image is fake or real using the OpenDeepfake-Preview dataset. The model uses the SiglipForImageClassification architecture.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

Experimental Model

Classification Report:
 precision recall f1-score support

 Fake 0.9718 0.9155 0.9428 10000
 Real 0.9201 0.9734 0.9460 9999

 accuracy 0.9444 19999
 macro avg 0.9459 0.9444 0.9444 19999
weighted avg 0.9459 0.9444 0.9444 19999

👁 download.png

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Label Space: 2 Classes

The model classifies an image as either:

Class 0: Fake 
Class 1: Real

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Install Dependencies

pip install -q transformers torch pillow gradio hf_xet

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Inference Code

import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/open-deepfake-detection" # Updated model name
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Updated label mapping
id2label = {
 "0": "Fake",
 "1": "Real"
}

def classify_image(image):
 image = Image.fromarray(image).convert("RGB")
 inputs = processor(images=image, return_tensors="pt")

 with torch.no_grad():
 outputs = model(**inputs)
 logits = outputs.logits
 probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

 prediction = {
 id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
 }

 return prediction

# Gradio Interface
iface = gr.Interface(
 fn=classify_image,
 inputs=gr.Image(type="numpy"),
 outputs=gr.Label(num_top_classes=2, label="Deepfake Detection"),
 title="open-deepfake-detection",
 description="Upload an image to detect whether it is AI-generated (Fake) or a real photograph (Real), using the OpenDeepfake-Preview dataset."
)

if __name__ == "__main__":
 iface.launch()

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Demo Inference

real

👁 Screenshot 2025-05-20 at 14-01-01 Deepfake Detection Model.png
👁 Screenshot 2025-05-20 at 14-01-41 Deepfake Detection Model.png

fake

👁 Screenshot 2025-05-20 at 14-04-22 Deepfake Detection Model.png
👁 Screenshot 2025-05-20 at 14-08-07 Deepfake Detection Model.png

Intended Use

open-deepfake-detection is designed for:

• Deepfake Detection – Identify AI-generated or manipulated images.
• Content Moderation – Flag synthetic or fake visual content.
• Dataset Curation – Remove synthetic samples from mixed datasets.
• Visual Authenticity Verification – Check the integrity of visual media.
• Digital Forensics – Support image source verification and traceability.

Base model

google/siglip2-base-patch16-512

(15)

image detection • 3 items • Updated 1 day ago