Phishing Webpage Detection via Cross-Page Visual Similarity Analysis with Large-Scale Image Encoders

Phishing poses a critical cyber security threat. Although machine learning-based detection methods have achieved notable success, visual similarity-based detection techniques still face the core challenge of complex visual feature extraction processes. This work innovatively introduces CLIP-series large models as image encoders. our approach achieves efficient phishing detection without requiring additional training. Experimental results demonstrate that our model achieves a classification-related accuracy rate of 91.21% on the VisualPhish dataset, significantly outperforming traditional models (more than 35% improvement). In phishing detection tasks, it attains a peak performance of 83%, surpassing the current state-of-the-art VisualPhishNet by 2%.