Identifying Dark Web Hidden Services with Novel Image Classes Using CNN and Quantum Transfer Learning

The dark web is an overwhelming and mysterious place that comprises hidden services. Dark web hidden services contain illegal or offensive content. Hidden services are not accessible through regular search engines or browsers and can only be accessed via specific software. The proposed work aims to identify these hidden services by analyzing their associated images and text data. Doing so, one can better understand the types of activities on the dark web and what kind of content is available. First, a dark web crawler is developed to collect dark web services. Images are then manually classified into four categories: Cards, Devices, Hackers, and Money. Next, preprocessing the collected dataset removed irrelevant images, and a Convolutional Neural Network (CNN) was trained to identify new dark web image classes. Finally, quantum Transfer Learning (QTL) improved the model’s performance. The proposed work goes beyond conventional methods of categorizing datasets by including new categories of image classes of dark web hidden services that have not been considered before. Also, the work examines image data and related text to establish a strong correlation between them. The proposed approach will provide insights into the dark web hidden service by confirming the relationship between the image and text data of the respective hidden-services.