PhishSnap: Image-Based Phishing Detection Using Perceptual Hashing

Md Abdul Ahad Minhaz, Zannatul Zahan Meem, Md. Shohrab Hossain
12/1/2025
cs.CRcs.CVcs.LG

No official code found. Best implementation: MinhazFuad/PhishSnap (0 stars, last commit 2025-12-06).

Abstract

Phishing remains one of the most prevalent online threats, exploiting human trust to harvest sensitive credentials. Existing URL- and HTML-based detection systems struggle against obfuscation and visual deception. This paper presents \textbf{PhishSnap}, a privacy-preserving, on-device phishing detection system leveraging perceptual hashing (pHash). Implemented as a browser extension, PhishSnap captures webpage screenshots, computes visual hashes, and compares them against legitimate templates to identify visually similar phishing attempts. A \textbf{2024 dataset of 10,000 URLs} (70\%/20\%/10\% train/validation/test) was collected from PhishTank and Netcraft. Due to security takedowns, a subset of phishing pages was unavailable, reducing dataset diversity. The system achieved \textbf{0.79 accuracy}, \textbf{0.76 precision}, and \textbf{0.78 recall}, showing that visual similarity remains a viable anti-phishing measure. The entire inference process occurs locally, ensuring user privacy and minimal latency.

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Code Implementations(1)

MinhazFuad/PhishSnapCommunity64%

PhishSnap is a browser-based phishing detection system that analyzes webpage screenshots using perceptual hashing and machine learning to classify sites as phishing or legitimate. arXiv paper: https://arxiv.org/abs/2512.02243

00CSS, HTMLNov 11, 202510 months ago

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Cite this paper

@article{minhaz2025phishsnap,
  title  = {PhishSnap: Image-Based Phishing Detection Using Perceptual Hashing},
  author = {Md Abdul Ahad Minhaz and Zannatul Zahan Meem and Md. Shohrab Hossain},
  year   = {2025},
  eprint = {2512.02243},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.02243v1}
}

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