用视觉哈希识别钓鱼网页，保护隐私且实时检测。
PhishSnap: Image-Based Phishing Detection Using Perceptual Hashing
- 通过截图与合法页面模板比对视觉哈希，检测伪装网页。
- 在1万条数据上达79%准确率，78%召回率，效果稳定。
- 本地运行不上传数据，适合注重隐私的用户使用。
网络钓鱼仍是主要在线威胁，利用用户信任窃取敏感信息。现有基于URL和HTML的检测系统难以应对混淆和视觉欺骗。本文提出PhishSnap，一种隐私保护的本地化钓鱼检测系统，采用感知哈希（pHash）技术。作为浏览器扩展，该系统捕获网页截图，计算视觉哈希，并与合法模板对比以识别视觉相似的钓鱼页面。构建了一个2024年包含10,000个URL的数据集（训练/验证/测试比例为70%/20%/10%），数据来源于PhishTank和Netcraft。由于安全下架，部分钓鱼页面无法获取，影响数据多样性。系统在测试集上实现0.79准确率、0.76精确率和0.78召回率，证明视觉相似性仍是有效的反钓鱼手段。整个推理过程在设备端完成，保障用户隐私并实现低延迟。
原文摘要 · Abstract (English)
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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