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.

翻译：近年来，垃圾邮件技术利用文本信息中的视觉效果，如文本投毒、词语混淆和隐藏文本加盐技术。这些效果能够规避基于文本的垃圾邮件检测技术。本文通过引入一种新颖的基于视觉的垃圾邮件检测架构（记为视觉垃圾邮件过滤器，VBSF）来克服这一局限。该多步骤过程模拟人眼处理视觉信息的自然方式，自动渲染接收到的电子邮件，并捕获其在用户屏幕上显示的内容。随后，两条不同的处理流程并行应用。第一条流程处理感知到的文本内容，包括使用光学字符识别（OCR）提取渲染后的文本内容，随后通过朴素贝叶斯（NB）和决策树（DT）内容分类器进行分类。第二条流程专注于邮件的外观，通过特定的卷积神经网络对渲染后的邮件图像进行分析和分类。最后，一个元分类器利用堆叠集成学习方法，整合基于文本和基于图像的分类器输出。对所提出的VBSF的性能评估表明，其在设计的数据集上达到了超过98%的准确率，高于所比较的现有技术。