For most of people, e-mail is the preferable medium for official communication. E-mail service providers face an endless challenge called spamming. Spammingis the exploitation of e-mail systems to send a bulk of unsolicited messages to a large number of recipients. Noisy image spamming is one of the new techniques to evade text analysis based and Optical Character Recognition (OCR) based spams filtering. In the present paper, Convolutional Neural Network (CNN) based on different color models was considered to address image spam problem. The proposed method was evaluated over a public image spam dataset. The results showed that the performance of the proposed CNN was affected by the color model used. The results also showed that XYZ model yields the best accuracy rate among all considered color models.

E-mail is an efficient and reliable data exchange service. Spams are undesired e-mail messages which are randomly sent in bulk usually for commercial aims. Obfuscated image spamming is one of the new tricks to bypass text-based and Optical Character Recognition (OCR)-based spam filters. Image spam detection based on image visual features has the advantage of efficiency in terms of reducing the computational cost and improving the performance. In this paper, an image spam detection schema is presented. Suitable image processing techniques were used to capture the image features that can differentiate spam images from non-spam ones. Weighted k-nearest neighbor, which is a simple, yet powerful, machine learning algorithm, was used as a classifier. The results confirm the effectiveness of the proposed schema as it is evaluated over two datasets. The first dataset is a real and benchmark dataset while the other is a real-like, modern, and more challenging dataset collected from social media and many public available image spam datasets. The obtained accuracy was 99.36% and 91% on benchmark and the proposed dataset, respectively.