Deep Fake Detector v2 Model EU AI Act Compliance Profile

Deep-Fake-Detector-v2-Model

Overview

The Deep-Fake-Detector-v2-Model is a state-of-the-art deep learning model designed to detect deepfake images. It leverages the Vision Transformer (ViT) architecture, specifically the google/vit-base-patch16-224-in21k model, fine-tuned on a dataset of real and deepfake images. The model is trained to classify images as either "Realism" or "Deepfake" with high accuracy, making it a powerful tool for detecting manipulated media.

Confusion Matrix:

<span style="color:red;">Update :</span> The previous model checkpoint was obtained using a smaller classification dataset. Although it performed well in evaluation scores, its real-time performance was average due to limited variations in the training set. The new update includes a larger dataset to improve the detection of fake images.

| Repository | Link | |------------|------| | Deep Fake Detector v2 Model | GitHub Repository |

Key Features

• Architecture: Vision Transformer (ViT) - google/vit-base-patch16-224-in21k.
• Input: RGB images resized to 224x224 pixels.
• Output: Binary classification ("Realism" or "Deepfake").
• Training Dataset: A curated dataset of real and deepfake images.

Your obligations as a Deep Fake Detector v2 Model deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Library

Base Model