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SPAMI: A cognitive spam protector for advertisement malicious images
Information Sciences Pub Date : 2020-06-18 , DOI: 10.1016/j.ins.2020.05.113
Aaisha Makkar , Neeraj Kumar , Albert Y Zomaya , Shalini Dhiman

In modern era, the graphical information is presented in the form of web images. As the dependency of human beings on web information is increasing day-by-day, so the spammers are injecting spam by adopting new spamming techniques. Image spam is a spamming technique that integrates spam text contents into graphical images in order to bypass conventional text-based spam filters. The spam images are of various categories, such as redirection spam, advertisement spam, fake review, and content spam. In order to detect image spam efficiently, it is important to analyze the features of the image data. However, the existing image spam detection techniques in literature focused on textual or graphic features of the image. Moreover, to extract the relevant features from the images is also a challenging task. So, to fill these gaps, in this paper, we propose a Spam Protector for Advertisement of Malicious Images (SPAMI) framework using features extraction by browsing different websites and webpages. SPAMI is a cognitive spam protector which labels the spam advertisement images by using deep learning models. Three deep learning models are used for the same, i.e., CNN, RNN, and LSTM. The regress analysis of output from these models is done in the proposed SPAMI framework. Finally, we analysed the labels (Advertisement, Suspicious, Normal) for all the 600 images collected. The accuracy obtained from these models is 95% with real-time collected images, which improved up to 97% when tested with ”Image Spam Hunter” dataset.



中文翻译:

SPAMI:认知垃圾邮件保护程序,用于广告恶意图像

在现代时代,图形信息以网络图像的形式呈现。随着人们对网络信息的依赖性日益增加,因此垃圾邮件发送者正在通过采用新的垃圾邮件发送技术来注入垃圾邮件。图像垃圾邮件是一种垃圾邮件技术,将垃圾邮件文本内容集成到图形图像中,以绕过常规的基于文本的垃圾邮件过滤器。垃圾邮件图像具有各种类别,例如重定向垃圾邮件,广告垃圾邮件,虚假评论和内容垃圾邮件。为了有效地检测图像垃圾邮件,分析图像数据的特征很重要。但是,文献中现有的图像垃圾邮件检测技术集中在图像的文本或图形特征上。此外,从图像中提取相关特征也是一项艰巨的任务。因此,为了填补这些空白,在本文中,我们建议使用通过浏览不同网站和网页进行特征提取的“恶意图片广告垃圾邮件保护器”(SPAMI)框架。SPAMI是一种认知垃圾邮件保护程序,它通过使用深度学习模型来标记垃圾邮件广告图像。相同的方式使用了三种深度学习模型,即CNN,RNN和LSTM。这些模型的输出的回归分析是在建议的SPAMI框架中完成的。最后,我们分析了收集的所有600张图像的标签(广告,可疑,正常)。从这些模型获得的实时采集图像的准确性为95%,在使用“ Image Spam Hunter”数据集进行测试时,可提高高达97%。SPAMI是一种认知垃圾邮件保护程序,它通过使用深度学习模型来标记垃圾邮件广告图像。相同的方式使用了三种深度学习模型,即CNN,RNN和LSTM。这些模型的输出的回归分析是在建议的SPAMI框架中完成的。最后,我们分析了收集的所有600张图像的标签(广告,可疑,正常)。从这些模型获得的实时采集图像的准确性为95%,在使用“ Image Spam Hunter”数据集进行测试时,该准确性提高了97%。SPAMI是一种认知垃圾邮件保护程序,它通过使用深度学习模型来标记垃圾邮件广告图像。相同的方式使用了三种深度学习模型,即CNN,RNN和LSTM。这些模型的输出的回归分析是在建议的SPAMI框架中完成的。最后,我们分析了收集的所有600张图像的标签(广告,可疑,正常)。从这些模型获得的实时采集图像的准确性为95%,在使用“ Image Spam Hunter”数据集进行测试时,可提高高达97%。正常)收集的所有600张图像。从这些模型获得的实时采集图像的准确性为95%,在使用“ Image Spam Hunter”数据集进行测试时,可提高高达97%。正常)收集的所有600张图像。从这些模型获得的实时采集图像的准确性为95%,在使用“ Image Spam Hunter”数据集进行测试时,可提高高达97%。

更新日期:2020-06-18
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