Classification of Image Spam Using Convolution Neural Network

Abstract

Image identification and classification is a basic issue in the fields of mainframe visualization and pattern recognition. In today’s world, a great deal of unwanted material is distributed via the Internet. The unwanted information contained inside images, i.e., image spam, endangers email-based communication systems. Unlike textural spam, image spam is difficult to be detected by many machine learning (ML) techniques. This paper intends to investigate and evaluate four deep learning (DL) methods that may be useful for image spam identification. Firstly, neural networks, especially deep neural networks, were trained on various image features. Their resilience was measured on an enhanced dataset, which was created specifically to outwit existing image spam detection methods. Next, a convolution neural network (CNN) was designed, and verified through experiments. Experimental results show that our novel approach for image spam identification outshines other current techniques in the field.