Image recognition model of fraudulent websites based on image leader decision and Inception-V3 transfer learning

Abstract

The fraudulent website image is a vital information carrier for telecom fraud. The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites. Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study, which have such disadvantages as difficulty in obtaining image data, insufficient image analysis, and single identification types. This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages. The data processing part of the model uses a breadth search crawler to capture the image data. Then, the information in the images is evaluated with the entropy method, image weights are assigned, and the image leader is selected. In model training and prediction, the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites. Using selected image leaders to train the model, multiple types of fraudulent websites are identified with high accuracy. The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.