Optimized File Type Detection and One-Shot Retrieval

File type classification is critical in digital forensics, and file carving. However, the increasing diversity of file formats challenges accurate classification. Traditional methods rely on hand-crafted features or compact neural networks but face long training times, limited training data, and lower accuracy. This paper introduces three novel, content-based file-type classification approaches to address these challenges. These approaches improve accuracy and streamline the integration of new file types using pre-trained models, enhancing both speed and reliability. The first approach utilizes Natural Language Processing (NLP) with a transformer architecture, while the second combines statistical features with a pre-trained model via transfer learning. These methods achieved accuracy rates of 72.4% and 69.2%, respectively, surpassing state-of-the-art Convolutional Neural Network (CNN) models. The third approach employs one-shot learning, achieving 100% accuracy in several scenarios, enabling efficient training with minimal data.