Deepfake Image Detection Using Fine-Tuned Vision Transformer (ViT)

This project focuses on detecting deepfake images using a fine-tuned version of the pre-trained model google/vit-base-patch16-224-in21k. The approach leverages the power of Vision Transformers (ViT) to classify images as real or fake.

Model Overview

Figure 1: Confusion matrix for test data

image/png

Figure 2: Confusion matrix for validation data

image/png

How to Use the Model

Below is an example of how to load and use the model for deepfake classification:

from transformers import AutoImageProcessor, AutoModelForImageClassificationimport torch
import torch
from PIL import Image

# Load the image_processor and model
image_processor = AutoImageProcessor.from_pretrained('ashish-001/deepfake-detection-using-ViT')
model = AutoModelForImageClassification.from_pretrained('ashish-001/deepfake-detection-using-ViT')
# Example usage
image = Image.open('path of the image')
inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
pred = torch.argmax(logits, dim=1).item()
label = 'Real' if pred == 1 else 'Fake'
print(f"Predicted type: {Label}")

Downloads last month
488
Safetensors
Model size
85.8M params
Tensor type
F32
ยท
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The model has no library tag.

Model tree for ashish-001/deepfake-detection-using-ViT

Finetuned
(2009)
this model

Space using ashish-001/deepfake-detection-using-ViT 1