Light-Weight File Fragments Classification Using Depthwise Separable Convolutions

Kunwar Muhammed Saaim, Muhamad Felemban*, Saleh Alsaleh, Ahmad Almulhem

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationICT Systems Security and Privacy Protection - 37th IFIP TC 11 International Conference, SEC 2022, Proceedings
EditorsWeizhi Meng, Simone Fischer-Hübner, Christian D. Jensen
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783031069741
StatePublished - 2022

Publication series

NameIFIP Advances in Information and Communication Technology
Volume648 IFIP
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Bibliographical note

Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.


  • Deep learning
  • Depthwise separable convolution
  • Digital forensics
  • File carving
  • File fragments classification

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management


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