  Correctly identifying the type of file under examination is a critical part
of a forensic investigation. The file type alone suggests the embedded content,
such as a picture, video, manuscript, spreadsheet, etc. In cases where a system
owner might desire to keep their files inaccessible or file type concealed, we
propose using an adversarially-trained machine learning neural network to
determine a file's true type even if the extension or file header is obfuscated
to complicate its discovery. Our semi-supervised generative adversarial network
(SGAN) achieved 97.6% accuracy in classifying files across 11 different types.
We also compared our network against a traditional standalone neural network
and three other machine learning algorithms. The adversarially-trained network
proved to be the most precise file classifier especially in scenarios with few
supervised samples available. Our implementation of a file classifier using an
SGAN is implemented on GitHub (https://ksaintg.github.io/SGAN-File-Classier).
