3 papers
cs.LG2026
Breaking Adversarial Transferability in Fine-Tuned Speech Recognition
Mojtaba Nafez, Aref Mousavi, Mohammad Ebrahim Mahdavi +3
Many organizations fine-tune publicly available pretrained Automatic Speech Recognition (ASR) models and deploy them in black-box settings, assuming limited access provides protect…
eess.AS2026
Transferable Adversarial Robustness for Speech Foundation Models via Hierarchical Stabilization
Aref Mousavi, Shahab Sherafat, Kiarash Kiani Feriz +4
Frozen speech foundation models (SFMs) make downstream adaptation efficient: the backbone can stay fixed while a task learns layer fusion and a lightweight classifier. Full adversa…
eess.AS2026
GhostWord: A Fine-Grained Backdoor Attack on Automatic Speech Recognition
Mojtaba Nafez, Mobina Poulaei, Kiarash Kiani Feriz +4
Automatic Speech Recognition (ASR) systems are widely deployed in safety-critical settings but remain vulnerable to data-poisoning backdoor attacks. Existing ASR backdoors typicall…