Improving Image Spam Detection Using a New Image Texture Features Selection

Spam is one of the problems that has plagued human societies. Although a lot of research has been done in this field, because spammers keep changing their methods like viruses, so there is always a need to provide new solutions in this field. The purpose of the research is to use image texture features to detect image spam. So far, 22 features of image texture have not been used in one place to detect image spam. In this paper, a hybrid method is used to extract key features. In the proposed hybrid method, the co-occurrence matrix of the gray level and chi-square and the threshold of changes in the value of the features are used. The steps mentioned have a great impact on the performance of the categories and improve the accuracy of detection. In the classification stage, the most widely used machine learning algorithms are used to detect image spams, and after obtaining the results of each category, the output of the algorithms used on spam and valid images is examined and compared. The obtained results show that with the help of the proposed method, good detection accuracy can be achieved compared to other methods. Among the reviewed algorithms, the neural network algorithm shows the best performance. The assumed algorithm in other articles shows a lower detection accuracy than the present article, but in the proposed method, it reaches 99.29% detection accuracy.