PROTECTOR: An optimized deep learning-based framework for image spam detection and prevention

Abstract

Image spam is a spamming technique that integrates spam text content into graphical images in order to bypass conventional text-based spam filters. In order to detect image spam efficiently, it is important to analyze the image data. The existing image spam detection techniques in literature focus on textual or graphic features of the image. None of the existing techniques considered the link information of the image which results in low accuracy and performance degradation. So, to fill these gaps, in this paper, we analyzed the link properties of image, for image spam detection and prevention. We propose an optimized framework called as PROTECTOR. In PROTECTOR, the rank score is generated by using the linkage information of the image, textual information of the image, and metadata information of the image. The computed rank score indicates the relevance of an image. This rank score is then used to train a deep neural network design of deep learning, which yields the accuracy of 96% with respect to various performance evaluation metrics. Also, the optimization algorithm, i.e., genetic algorithm is fitted in the results according to the defined fitting function. The proposed framework is validated with standard dataset of Image spam Hunter.