A Quishing Detection Model via Global Attention-Based Adaptive Fusion of QR Code and URL Text

Quishing (QR code phishing) represents an emerging cybersecurity threat characterized by its dual-modal nature. This attack simultaneously exploits the visual modality of QR code images and the semantic modality of embedded URLs to launch covert attacks. Such a dual-modal attack strategy renders existing single-modality detection methods (relying solely on either images or URLs) and traditional fusion approaches based on expert-driven feature engineering fundamentally inadequate for comprehensive defense. To address this gap, this study proposes QRURL-GAAFNet (QR Code and Associated URL Features Global Attention-Based Adaptive Feature Fusion Network), which, to the best of our knowledge, represents the first end-to-end deep learning framework that holistically addresses the unique cross-modal correlation patterns inherent in Quishing attacks. Within this framework, dedicated deep learning networks independently extract visual features from QR codes and semantic features from URLs. Unlike conventional multimodal fusion approaches that treat modalities independently or use fixed fusion strategies, our proposed Global Attention-based Adaptive Feature Fusion (GAAF) mechanism dynamically learns sample-specific cross-modal dependencies by computing adaptive fusion weights from joint representations. By emphasizing discriminative information based on learned cross-modal correlations, this adaptive weighting strategy enables the model to focus on attack-relevant signatures that emerge from the interaction between QR code visual artifacts and URL semantic obfuscation patterns. Experimental results on public dataset demonstrate that QRURL-GAAFNet outperforms state-of-the-art methods in Quishing detection. Moreover, comprehensive experiments on the effectiveness of the data increment mechanism and robustness analysis, including evaluations against QR code image perturbations and URL text obfuscation attacks, validate its strong practical applicability in real-world security scenarios.