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 : Update : 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. Fine Tuning : The model is fine tuned using Hugging Face's Trainer API with advanced…
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