论文标题
图像垃圾邮件检测的卷积神经网络
Convolutional Neural Networks for Image Spam Detection
论文作者
论文摘要
垃圾邮件可以定义为未经请求的批量电子邮件。为了逃避基于文本的过滤器，垃圾邮件发送者有时将垃圾邮件文本嵌入图像中，该文本称为图像垃圾邮件。在这项研究中，我们根据图像分析考虑了图像垃圾邮件检测的问题。我们将卷积神经网络（CNN）应用于此问题，我们将使用CNN获得的结果与其他机器学习技术进行了比较，并将结果与以前的相关工作进行了比较。我们考虑现实世界的图像垃圾邮件和具有挑战性的图像垃圾邮件样数据集。我们通过基于新型功能集的新功能组组成的组合，通过使用CNN来改善以前的工作。
Spam can be defined as unsolicited bulk email. In an effort to evade text-based filters, spammers sometimes embed spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on image analysis. We apply convolutional neural networks (CNN) to this problem, we compare the results obtained using CNNs to other machine learning techniques, and we compare our results to previous related work. We consider both real-world image spam and challenging image spam-like datasets. Our results improve on previous work by employing CNNs based on a novel feature set consisting of a combination of the raw image and Canny edges.
