The Generalization Gap: Do Audio Deepfake Detectors Actually Protect Against Modern Vishing?

V. García Martínez-Echevarría, R. Palacios, G. López, A. Gupta

Electronics Vol. 15, nº. 13, pp. 2846

Resumen:

Voice phishing, commonly known as vishing, has become one of the fastest-growing threats in social engineering. The rapid advancement and accessibility of AI voice cloning tools have enabled attackers to produce highly convincing synthetic speech at minimal cost, driving a sharp increase in impersonation fraud. Accordingly, automatic detection of synthetic voices could contribute, as one component of a broader defense, to mitigating vishing attacks. This paper studies the automatic detection of AI-generated speech, with a particular focus on how well such detectors generalize beyond their training data to modern, unseen synthesis methods. Two detection approaches are evaluated: a Residual CNN (convolutional neural network) trained as a binary classifier on three different time–frequency representations and a one-class learning strategy with a ResNet-18 backbone, yielding four models in total. Models were trained on the well-known ASVspoof 2019 Logical Access dataset and tested on its standard partitions. Then, models were tested on the SONAR benchmark, which gathers voices generated with state-of-the-art synthesis techniques unseen during training. Experimental results show that, on the modern systems gathered in SONAR, all four configurations fall close to chance. The LFCC one-class detector generalizes comparatively best, but the apparently higher accuracy of some models reflects a tendency to label most speech as spoofed. These findings indicate that the evaluated detectors can provide, at most, a partial security layer against vishing driven by current and emerging speech-synthesis technologies, although continuous model updates are recommended.

Resumen divulgativo:

Este estudio evalúa detectores de voz sintética para combatir el vishing. Los modelos entrenados con ASVspoof 2019 funcionaron bien en datos conocidos, pero generalizaron mal a voces modernas de SONAR, evidenciando la necesidad de actualizaciones continuas.

Palabras Clave: AI-generated speech; spoofing detection; residual CNN (convolutional neural network); one-class learning; generalization; vishing

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

Referencia DOI: https://doi.org/10.3390/electronics15132846

Publicado en papel: Julio 2026.

Publicado on-line: Junio 2026.

Cita:
V. García Martínez-Echevarría, R. Palacios, G. López, A. Gupta, "The Generalization Gap: Do Audio Deepfake Detectors Actually Protect Against Modern Vishing?", Electronics, Vol. 15, nº. 13, pp. 2846, Julio 2026. [Online: Junio 2026] doi: 10.3390/electronics15132846