This paper introduces GenDF, a generalized deepfake detection framework that leverages a fine-tuned Vision Transformer (ViT) to identify subtle patch discontinuities in fake images and continuities in real ones. It employs deepfake-specific representation learning, feature space redistribution, and classification-invariant feature augmentation to achieve state-of-the-art generalization across various unseen deepfake patterns with minimal trainable parameters.