VBSF: A Visual-Based Spam Filtering Technique for Obfuscated Emails

TL;DR

This paper introduces VBSF, a novel visual-based spam filtering system that mimics human visual processing to detect obfuscated spam emails, achieving over 98% accuracy by combining OCR, traditional classifiers, and CNNs.

Contribution

The paper presents a new multi-modal spam detection architecture that effectively captures visual obfuscation techniques used in spam emails, outperforming existing text-based methods.

Findings

Achieves over 98% accuracy on the designed dataset.

Combines OCR, traditional classifiers, and CNNs for improved detection.

Outperforms existing techniques in spam detection accuracy.

Abstract

Recent spam email techniques exploit visual effects in text messages, such as poisoning text, obfuscating words, and hidden text salting techniques. These effects were able to evade spam detection techniques based on the text. In this paper, we overcome this limitation by introducing a novel visual-based spam detection architecture, denoted as visual-based spam filter (VBSF). The multi-step process mimics the human eye's natural way of processing visual information, automatically rendering incoming emails and capturing their content as it appears on a user screen. Then, two different processing pipelines are applied in parallel. The first pipeline pertains to the perceived textual content, as it includes optical character recognition (OCR) to extract rendered textual content, followed by naive Bayes (NB) and decision tree (DT) content classifiers. The second pipeline focuses on the appearance of the email, as it analyzes and classifies the images of rendered emails through a specific convolutional neural network. Lastly, a meta classifier integrates text- and image-based classifier outputs, exploiting the stacking ensemble learning method. The performance of the proposed VBSF is assessed, showing that it achieves an accuracy of more than 98%, which is higher than the compared existing techniques on the designed dataset.