# Perceptual Hashing applied to Tor domains recognition

Source: [Perceptual Hashing applied to Tor domains recognition](<https://www.sota2.com/research/paper/68fa67971e209131cea173f9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:43.018Z

Authors: Rubel Biswas, Roberto A. Vasco-Carofilis, Eduardo Fidalgo Fernandez, Francisco J\\'a\\~nez Martino, Pablo Blanco Medina

Published: 2020-05-20T14:53:36.000Z

## Abstract

The Tor darknet hosts different types of illegal content, which are monitored by cybersecurity agencies. However, manually classifying Tor content can be slow and error-prone. To support this task, we introduce Frequency-Dominant Neighborhood Structure (F-DNS), a new perceptual hashing method for automatically classifying domains by their screenshots. First, we evaluated F-DNS using images subject to various content preserving operations. We compared them with their original images, achieving better correlation coefficients than other state-of-the-art methods, especially in the case of rotation. Then, we applied F-DNS to categorize Tor domains using the Darknet Usage Service Images-2K (DUSI-2K), a dataset with screenshots of active Tor service domains. Finally, we measured the performance of F-DNS against an image classification approach and a state-of-the-art hashing method. Our proposal obtained 98.75% accuracy in Tor images, surpassing all other methods compared.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2005.10090>)

[PDF](<https://arxiv.org/pdf/2005.10090.pdf>)