Multi-Signal Learning Framework for Robust Detection of Visually Deceptive Text

A. Jovanovic-Hacon, S.W. Luo, C. Cao, M. Wanderley, H. Boriel, I. Tenison, H. Kim, F.S. Beserra, M.A. Ferreira, R. Palacios, A. Gupta

Electronics Vol. 15, nº. 19, pp. 4531

Resumen:

Visually deceptive text, such as homoglyph-based and near-duplicate variations, poses a significant challenge in applications ranging from financial systems to online identity verification. Existing approaches rely on either string-based similarity metrics or learned embeddings, but each captures only a subset of the information required for robust spoof detection. In this work, we show that these methods are complementary: embedding-based models capture perceptual similarity but struggle under structural divergence, while string-based methods capture character-level patterns but fail under visually deceptive transformations. Motivated by this observation, we propose a unified multi-signal learning framework that integrates visually aligned text embeddings with string-based features such as edit distance and token overlap through a learned fusion model. Experiments run on a large homoglyph-domain benchmark show that the proposed method consistently outperforms both string-based and embedding-based baselines, with the largest gains in challenging cases where individual methods fail. The approach is motivated by deployment scenarios such as corporate email gateways and DNS or registrar-side monitoring pipelines, where lightweight detection before delivery or registration would be valuable, although additional techniques to deceive domain names should be considered for an efficient implementation.

Palabras Clave: homoglyph detection; visual text alignment contrastive learning; curriculum learning; perceptual similarity; representation learning; vision–language models

Índice de impacto JCR-JIF y cuartil WoS: 2,900 - Q2 (2025)

Referencia DOI: https://doi.org/10.3390/electronics15194531

Publicado en papel: Octubre 2026.

Publicado on-line: Octubre 2026.

Cita:
A. Jovanovic-Hacon, S.W. Luo, C. Cao, M. Wanderley, H. Boriel, I. Tenison, H. Kim, F.S. Beserra, M.A. Ferreira, R. Palacios, A. Gupta, "Multi-Signal Learning Framework for Robust Detection of Visually Deceptive Text", Electronics, Vol. 15, nº. 19, pp. 4531, Octubre 2026. [Online: Octubre 2026] doi: 10.3390/electronics15194531