Fusion of Text and Image Features: A New Approach to Image Spam Filtering

While enjoying the convenience of email communications, many users have also experienced annoying email spam. Even if the current spam detecting approaches have gained a competitive edge against text-based email spam, they still face the challenge arising from imagebased spam (image spam in short). Image spam normally includes embedded images that contain the spam messages in binary format rather than text format and cost more storage and bandwidth resources. In this paper, we propose a hybrid image spam filtering framework to detect spam images based on both extracted text and image features. Our experimental results show that our approach achieves significant improvement in detection accuracy as compared with other methods that simply use text or image features, and works robustly in an environment with either complex background or compression artifact.