翻译为中文In the article Identifying Gambling and Porn Websites with Image Recognition[1], after analyzing the current methods of website classification, it is argued that the text content based methods suffer from the curse of dimensionality because the websites become more and more complex and massive. A visual content-based website classification was selected to identify gambling and pornographic websites. The SURF model parameters in the BOW model were found to be effective in identifying screenshots of gambling and pornographic websites, and were implemented to distinguish gambling and pornographic websites from normal websites using support vector machines (SVM) by extracting visual features from each webpage screenshot based on the BoW model.
	The article Automatic Detection of Pornographic and Gambling Websites Based on Visual and Textual Content Using a Decision Mechanism[2], proposed the use of Doc2Vec to learn the textual features of the textual content in the hypertext markup language (HTML) source code of a website。In addition, the traditional bag of visual words (BoVW) is improved to better represent the visual features of web screenshots by introducing local spatial relationships of feature points. Then, based on these two types of features, a text classifier and an image classifier are trained to make predictions through a decision mechanism. In the decision mechanism, a data fusion algorithm based on logistic regression(LR) was designed to obtain the final prediction result by measuring the contribution of the two classification results to the final category prediction and the results are experimented to verify the superiority of this method.
	The article Automatic Image Annotation based on Co-Training[3] points out that training a well-performing model usually requires a large number of labeled samples, and manual annotation is costly, so a new semi-supervised automatic image annotation method based on a co-training algorithm is proposed, which can utilize both labeled and The performance is improved by constructing two different classifiers using the co-training method, and the experimental results show that the method has better labeling performance than many existing automatic image standard methods.
Through reading the above-mentioned literature, we understand that the following problems still exist in the current research on the identification of blackmail websites at home and abroad: (1) insufficient use of webpage content, lack of research on the extraction of a large amount of available textual information in webpage screenshots; (2) using only the unimodal data of images, the identification accuracy needs to be improved, and the second article combines two types of features to improve the identification accuracy to a certain extent. (3) Few sample data with labels, high labor and time cost to build the dataset.
By reading the above literature, we can find that the current identification for blackmail websites can be mainly based on visual features and text features, and there are various classification methods for text and images, and most of the current research is based on a single feature or applying a single method for classification and identification. Inspired by the second literature, we can combine image and text features and combine the current research on text classification and image classification based on deep learning, which has outstanding performance in this field, to improve the accuracy of recognition and classification. Through the third article, we can apply collaborative training algorithm to improve the generalization of the model and optimize the model.