Spam Email Image Detection Using Convolution Neural Network and Convolutional Block Attention Module

Image spam is continually a popular area of research. Cyberspace is under attack from many different directions. Spam that contains text embedded in an image is known as “image spam”. The rise in online conversation through email has globally contributed to the increasing rate of spam email relatively. First started text spam then now a new challenge in few years image spam which has been a major problem in the field of computing. Various machine learning techniques are used to classify image spam based on a large number of attributes retrieved from the image. Most existing image spam filtering systems use features created by hand and time-consuming machine learning approaches. Convolution neural networks (CNNs) are commonly utilized in image-processing, classification, and feature extraction applications due to their outstanding results. In this research, we use a two CNN model built using deep learning methods to analyze image spam; one is without attention, and next one is with an attention module that time. We use the convolutional block attention module (CBAM); this module attention only spams area of the image and activities of the better performance. Our proposed method achieved very competitive performance—99.42% accuracy on the Image Spam Hunter (ISH) dataset and state-of-the-art performance; in this paper, we used the ISH dataset.