  With the open-source revolution, source codes are now more easily accessible
than ever. This has, however, made it easier for malicious users and
institutions to copy the code without giving regards to the license, or credit
to the original author. Therefore, source code author identification is a
critical task with paramount importance. In this paper, we propose ICodeNet - a
hierarchical neural network that can be used for source code file-level tasks.
The ICodeNet processes source code in image format and is employed for the task
of per file author identification. The ICodeNet consists of an ImageNet trained
VGG encoder followed by a shallow neural network. The shallow network is based
either on CNN or LSTM. Different variations of models are evaluated on a source
code author classification dataset. We have also compared our image-based
hierarchical neural network model with simple image-based CNN architecture and
text-based CNN and LSTM models to highlight its novelty and efficiency.
