SPAMI: A cognitive spam protector for advertisement malicious images

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.