5 papers
Room for Error: Large-Scale Simulation of Over-the-Air Acoustic Attacks
Andrew C. Cullen, Neil G. Marchant, Jiani Xie +4
While voice control is rapidly becoming a ubiquitous vector of human-AI communication, the risks facing these systems remain poorly understood. This is, in part, a product of the d…
What Was That Again? Certified Robustness for Automatic Speech Recognition
Andrew C. Cullen, Neil G. Marchant, Jiani Xie +2
Automatic Speech Recognition systems are notoriously both sensitive to adversarial and benign perturbations. While this has been repeatedly demonstrated using reference datasets, d…
Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks
Jiani Xie, Andrew C. Cullen, Paul Montague +1
Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transcription decisions are inherent…
Semantic-aware Adversarial Fine-tuning for CLIP
Jiacheng Zhang, Jinhao Li, Hanxun Huang +3
Recent studies have shown that CLIP model's adversarial robustness in zero-shot classification tasks can be enhanced by adversarially fine-tuning its image encoder with adversarial…
One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy
Jiacheng Zhang, Benjamin I. P. Rubinstein, Jingfeng Zhang +1
Statistical adversarial data detection (SADD) detects whether an upcoming batch contains adversarial examples (AEs) by measuring the distributional discrepancies between clean exam…