Light-Weight File Fragments Classification Using Depthwise Separable Convolutions

In digital forensics, classification of file fragments is an important step to complete the file carving process. There exist several approaches to identify the type of file fragments without relying on meta-data. Examples of such approaches are using features like header/footer and N-gram to identify the fragment type. Recently, deep learning models have been successfully used to build classification models to achieve this task. In this paper, we propose a light-weight file fragment classification using depthwise separable convolutional neural network model. We show that our proposed model does not only yield faster inference time, but also provide higher accuracy as compared to the state-of-art convolutional neural network based models. In particular, our model achieves an accuracy of 78.45% on the FFT-75 dataset with 100K parameters and 167M FLOPs, which is 24 × faster and 4–5 × smaller than the state-of-the-art classifier in the literature.