  Machine learning is penetrating various domains virtually, thereby
proliferating excellent results. It has also found an outlet in digital
forensics, wherein it is becoming the prime driver of computational efficiency.
A prominent feature that exhibits the effectiveness of ML algorithms is feature
extraction that can be instrumental in the applications for digital forensics.
Convolutional Neural Networks are further used to identify parts of the file.
To this end, we observed that the literature does not include sufficient
information about the identification of the algorithms used to compress file
fragments. With this research, we attempt to address this gap as compression
algorithms are beneficial in generating higher entropy comparatively as they
make the data more compact. We used a base dataset, compressed every file with
various algorithms, and designed a model based on that. The used model was
accurately able to identify files compressed using compress, lzip and bzip2.
