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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…
cs.CL2026
Know When to Hold 'em: Correct-Token Retention in Uniform-State Diffusion Language Models
Mojtaba Nafez, James Henderson
Uniform-state diffusion models (USDMs) can revise any token at any denoising step, which lets them correct their own mistakes, a key advantage over masked diffusion. Self-correctio…
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…