Convolutional Neural Networks for Image Spam Detection
This addresses the problem of evading text-based spam filters for email users, but it is incremental as it builds on existing CNN methods with a new feature combination.
The paper tackled image spam detection by applying convolutional neural networks (CNNs) with a novel feature set combining raw images and Canny edges, achieving improved results compared to previous work and other machine learning techniques.
Spam can be defined as unsolicited bulk email. In an effort to evade text-based filters, spammers sometimes embed spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on image analysis. We apply convolutional neural networks (CNN) to this problem, we compare the results obtained using CNNs to other machine learning techniques, and we compare our results to previous related work. We consider both real-world image spam and challenging image spam-like datasets. Our results improve on previous work by employing CNNs based on a novel feature set consisting of a combination of the raw image and Canny edges.