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

Shengli Zhou, Cheng Xu, Rui-Lin Xu, Weijie Ding, Chao Chen, Xiaoyang Xu
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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.
基于图像领导者决策和 Inception-V3 转移学习的欺诈网站图像识别模型
欺诈网站图片是电信欺诈的重要信息载体。高效、准确地识别欺诈网站图像是打击和处理欺诈网站的关键。目前对诈骗网站图像识别的研究主要停留在图像特征提取和相似性研究层面,存在图像数据获取困难、图像分析不充分、识别类型单一等缺点。本研究针对这些缺点,开发了基于熵法的图像领导者决策模型和 Inception-v3 转移学习模型。模型的数据处理部分使用广度搜索爬虫来获取图像数据。然后,使用熵法评估图像中的信息,分配图像权重,并选出图像领导者。在模型训练和预测中,Inception-v3 模型的迁移学习被引入到欺诈网站的图像识别中。利用选定的图像领导者来训练模型,可以高精度地识别出多种类型的欺诈网站。实验证明,与其他现有模型相比,该模型在识别欺诈网站图像方面具有更高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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