arXiv:2607. 01702v1 Announce Type: cross Abstract: Recently, speech classification methods have gained widespread adoption in intelligent gadgets.
By Yueming Huang, Wenhan Yao, Fen Xiao, Xiarun Chen, Weiping Wen
arXiv:2607. 15724v1 Announce Type: cross Abstract: With the rapid development of deep learning, its vulnerability has gradually emerged in recent years.
By Jinwen Xin, Xixiang Lyu, Jing Ma
arXiv:2607. 15697v1 Announce Type: cross Abstract: Backdoor attacks pose a critical threat to neural network models, allowing attackers to implant a backdoor during the training phase by manipulating a small portion of the training data.
By Jinwen Xin, Xixiang Lv
The paper investigates poisoning-based backdoor attacks on Speech Emotion Recognition (SER) systems that use self‑supervised acoustic representations. It introduces a stealthy, low‑energy acoustic trigger that can be embedded imperceptibly into both natural and synthetic speech, enabling scalable poisoning. Experiments show high attack success rates with low poisoning ratios, cross‑model transferability, and a particular vulnerability of self‑supervised representations, highlighting the lowered barrier to effective backdoor attacks via TTS technology.
By Yongbin Huang, Xihao Xie, Jia Zhang
arXiv:2607. 01729v1 Announce Type: new Abstract: Deep learning models for speech classification are vulnerable to backdoor attacks, where malicious triggers cause misclassification at inference time.
By Yueming Huang, Wenhan Yao, Fen Xiao, Xiarun Chen, Weiping Wen
The paper introduces an adaptive jailbreak attack framework that evaluates both cascaded pipelines and end‑to‑end large audio‑language models (LALMs) under a unified setting. It employs a feedback‑guided mutation engine to automatically generate and refine jailbreak candidates across textual prompts and audio perturbations, thereby broadening attack diversity. Experiments on six audio‑based systems show that both paradigms remain highly vulnerable, with the framework achieving higher attack success rates than existing methods.
By Linghan Huang, Bo Li, Huaming Chen, Kim-Kwang Raymond Choo