Spam image detection based on convolutional block attention module

Abstract

Digital communication platforms, such as Gmail and Yahoo, are become essential in our professional and personal lives. In addition to the low cost of e-mails, they are fast. Despite the advantages of these tools, spammers try to send unsolicited e-mail, known as spam, daily. Recently, image spam, a new type of spam e-mail, is developed by spammers in order to avoid detection based on text-based spam filtering systems. Image spam contains more complex information as compared to text spam. For this reason, the detection of image spam is still a challenging task for researchers. Most of the developed image spam filtering systems are based on hand-crafted features and machine learning techniques, which are time-consuming and less efficient. In addition, these systems do not focus on the important features, which can have an impact on the detection process. In this paper, we apply the convolutional block attention module (CBAM) model in order to address the problem of image spam. The experiments are conducted on the available dataset, called image spam hunter (ISH). The results obtained are then compared, using the CBAM model, to other existing state-of-the-art methods. The results obtained have demonstrated that the convolutional block attention module (CBAM) is efficient for image spam detection.