Document Type

Contribution to a Book

Publication Date


Publication Title

Malware Analysis Using Artificial Intelligence and Deep Learning


Mark Stamp, Mamoun Alazab, Andrii Shalaginov

First Page


Last Page





Image classification is a fundamental problem of computer vision and pattern recognition. We focus on images that contain spam. Spam is unwanted bulk content, and image spam is unwanted content embedded inside the images. Image spam potentially creates a threat to the credibility of any email-based communication system. While a lot of machine learning techniques are successful in detecting textual based spam, this is not the case for image spams, which can easily evade these textual-spam detection systems. In our work, we explore and evaluate four deep learning techniques that detect image spams. First, we train deep neural networks using various image features. We explore their robustness on an improved dataset, which was especially build in order to outsmart current image spam detection techniques. Finally, we design two convolution neural network architectures and provide experimental results for these, alongside the existing VGG19 transfer learning model, for detecting image spams. Our work offers a new tool for detecting image spams, usage of a bigger dataset, and is compared against recent related tools.


This is a post-peer-review, pre-copyedit version of a chapter published in Malware Analysis Using Artificial Intelligence and Deep Learning. The final authenticated version is available online at:

SJSU users: Use the following link to login and access the article via SJSU databases.