Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models

Phishing attacks pose a significant threat to Internet users, with cybercriminals elaborately replicating the visual appearance of legitimate websites to deceive victims. Visual similarity-based detection systems have emerged as an effective countermeasure, but their effectiveness and robustness in real-world scenarios have been underexplored. Therefore, we evaluate the effectiveness and robustness of popular visual similarity-based anti-phishing models using these datasets. The shared project contains both reimplement codes and datasets.网络钓鱼攻击对互联网用户构成重大威胁，网络犯罪分子会精细复刻合法正规网站的视觉外观以诱骗受害者。基于视觉相似性的钓鱼检测系统已成为有效的防御手段，但这类系统在真实场景中的有效性与鲁棒性尚未得到充分探索。因此，本研究利用本数据集对主流基于视觉相似性的反钓鱼模型的有效性与鲁棒性展开评估。本共享项目同时包含复现代码与数据集。