File Fragment Classification using Light-Weight Convolutional Neural Networks. (arXiv:2305.00656v1 [cs.CR])

In digital forensics, file fragment classification is an important step
toward completing file carving process. There exist several techniques to
identify the type of file fragments without relying on meta-data, such as using
features like header/footer and N-gram to identify the fragment type. Recently,
convolutional neural network (CNN) models have been used to build
classification models to achieve this task. However, the number of parameters
in CNNs tends to grow exponentially as the number of layers increases. This
results in …

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