An Image Spam Classification Model Based on File Features Using Neural Networks

Spam is an unauthorized intrusion into a virtual space, which caused serious economy loss and social issues. Recently, Spammers have spreading new kind of email spamming method called image spamming, which uses simple image processing technologies like varied borders or backgrounds, randomly varied spacing or margins, and adding artifacts to the images. Priceless effort, time, and money of the users and organizations are wasted in handling them. Because of the recent upsurge in image spam, the proposed system is developed to classify image spam based on file features of an image, rather than text contents by using Back propagation neural networks, which classify the incoming image as a spam or ham. The experimental result show the system correctly classifies 95% of spam images with minimum false positives.