Backdooring Acoustic Foundation Models for Physically Realizable Triggers
Despite making minimal assumptions about adversary capabilities (e.g., no access to pre-training data), we show that FAB preserves benign performance while inducing backdoors that survive fine-tuning and cause significant degradation across diverse downstream tasks when activated.
ProofPaper ↗
Key points
- Acoustic foundation models (AFMs) have democratized acoustic applications, enabling powerful models for tasks ranging from speech recognition to speaker verification with minimal resources.
- However, the security of applications based on AFMs remains largely underexplored.
- Our work addresses this gap by proposing the Foundation Acoustic model Backdoor (FAB) attack, demonstrating that state-of-the-art AFMs are susceptible to backdooring under practical settings.
- We evaluate FAB using two leading AFMs, nine downstream tasks, and four different triggers.
Sources (1)
- [1]Backdooring Acoustic Foundation Models for Physically Realizable TriggersarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 10:41 AM
Despite making minimal assumptions about adversary capabilities (e.g., no access to pre-training data), we show that FAB preserves benign performance while inducing backdoors that survive fine-tuning and cause significant degradation across diverse downstream tasks when activated.
Acoustic foundation models (AFMs) have democratized acoustic applications, enabling powerful models for tasks ranging from speech recognition to speaker verification with minimal resources.
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