Deepfake-Quality-Assess-Siglip2

Deepfake-Quality-Assess-Siglip2

Deepfake-Quality-Assess-Siglip2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to assess the quality of deepfake images using the SiglipForImageClassification architecture.

The model categorizes images into two classes:

• Class 0: "Issue in Deepfake" – indicating that the deepfake image has noticeable flaws or inconsistencies.
• Class 1: "High-Quality Deepfake" – indicating that the deepfake image is of high quality and appears more realistic.

Run with Transformers🤗

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Deepfake-Quality-Assess-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def deepfake_detection(image):
    """Predicts deepfake probability scores for an 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()
    
    labels = model.config.id2label
    predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
    
    return predictions

# Create Gradio interface
iface = gr.Interface(
    fn=deepfake_detection,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(label="Prediction Scores"),
    title="Deepfake Quality Detection",
    description="Upload an image to check its deepfake probability scores."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()

Intended Use:

The Deepfake-Quality-Assess-Siglip2 model is designed to evaluate the quality of deepfake images. It helps distinguish between high-quality deepfakes and those with noticeable issues. Potential use cases include:

• Deepfake Quality Assessment: Identifying whether a generated deepfake meets high-quality standards or contains artifacts and inconsistencies.
• Content Moderation: Assisting in filtering low-quality deepfake images in digital media platforms.
• Forensic Analysis: Supporting researchers and analysts in assessing the credibility of synthetic images.
• Deepfake Model Benchmarking: Helping developers compare and improve deepfake generation models.