Peng Cheng

STEP introduces a novel, black-box, retraining-free detector that profiles audio samples using dual perturbation branches to detect backdoor attacks by exploiting the characteristic instability of hidden triggers.

This paper introduces personalized mechanisms for estimating streaming statistics under $w$-event personalized differential privacy, significantly improving accuracy compared to existing methods.

The paper introduces ToxiAlert-Bench, a large-scale audio dataset that uniquely annotates both textual and paralinguistic sources of toxicity, and proposes a dual-head neural network that significantly improves speech toxicity detection by leveraging these paralinguistic cues.