Detecting Computer Generated Images for Image Spam Filtering

Image spam continues to be one of cyber security problem today. Spammers used image spam as a technique to by-pass conventional email filters. Anti-Spammers used image classification as a method to detect images spam by extracting different features of the image. One of the important features used is color features. Several works used different color analysis to differentiate image spam, most of these works used supervised methods trying to differentiate computer generated images which is mostly like to be a spam and natural images. Supervised methods have its weaknesses, such as high cost in computation, requires training data, and rapid changes in spammers behaviors. This paper develops an unsupervised method using HSL geometric model (Hue, Saturation, and Luminance) to distinguish computer generated (CG) and natural images. Rules and Heuristics are defined by using HSL variables. The proposed method mainly depends on Saturation and Lightness values and their histograms. Experiment results shows that the combination of these variables can give high classification accuracy results.