{"ID":2834735,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.02243","arxiv_id":"2512.02243","title":"PhishSnap: Image-Based Phishing Detection Using Perceptual Hashing","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.","short_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 leveragi...","url_abs":"https://arxiv.org/abs/2512.02243","url_pdf":"https://arxiv.org/pdf/2512.02243v1","authors":"[\"Md Abdul Ahad Minhaz\",\"Zannatul Zahan Meem\",\"Md. Shohrab Hossain\"]","published":"2025-12-01T22:15:12Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false}