A significant element of digital forensics is the classification of file fragments. Researchers have used several approaches to classify file fragments without using meta-data. Recently, deep learning algorithms, including convolutional neural networks and feed-forward neural networks, have been used to build classification models for this task. This paper proposes a depthwise separable convolutional neural network-based model for the efficient classification of file fragments. Our proposed model's evaluation results are faster and more accurate than state-of-the-art models on 75 file fragment types. In particular, our model achieves an accuracy of 78.45\% on the FFT-75 dataset with 100K parameters and 167M FLOPs, which is 24x faster and 4-5x smaller than the state-of-the-art classifier in the literature.

We used FFT-75 dataset that composed of 75 types of files that are organized into 6 different scenarios and variants with 512 and 4096-byte blocks.