Transferable Adversarial Robustness for Speech Foundation Models via Hierarchical Stabilization
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 adversarial fine-tuning is a standard route to robustness, but generating adversarial examples and updating the backbone for every task sacrifices that efficiency. We ask whether robustness can instead be learned before future tasks are known. For a frozen backbone and linear classifier, robustness can be understood through the interaction between representation stability and decision-boundary margin. This leads directly to our design: we stabilize representations across the hidden layers, rather than only the final la...
| Authors | Aref Mousavi, Shahab Sherafat, Kiarash Kiani Feriz et al. |
|---|---|
| Primary category | eess.AS |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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