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20192025
most citedSLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems

7 citations · 24 across the 10 of their papers we have counts for

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Showing cs.CRShow all

6 papers · 1 filter

cs.CR2025

AudioJailbreak: Jailbreak Attacks against End-to-End Large Audio-Language Models

Guangke Chen, Fu Song, Zhe Zhao +7

Jailbreak attacks to Large audio-language models (LALMs) are studied recently, but they exclusively focused on the attack scenario where the adversary can fully manipulate user pro…

cs.CR2024

LaserGuider: A Laser Based Physical Backdoor Attack against Deep Neural Networks

Yongjie Xu, Guangke Chen, Fu Song +1

Backdoor attacks embed hidden associations between triggers and targets in deep neural networks (DNNs), causing them to predict the target when a trigger is present while maintaini…

cs.CR2023★ 7 cited

SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems

Guangke Chen, Yedi Zhang, Fu Song

Membership inference attacks allow adversaries to determine whether a particular example was contained in the model's training dataset. While previous works have confirmed the feas…

cs.CR2023★ 3 cited

QFA2SR: Query-Free Adversarial Transfer Attacks to Speaker Recognition Systems

Guangke Chen, Yedi Zhang, Zhe Zhao +1

Current adversarial attacks against speaker recognition systems (SRSs) require either white-box access or heavy black-box queries to the target SRS, thus still falling behind pract…

cs.CR2021★ 6 cited

SEC4SR: A Security Analysis Platform for Speaker Recognition

Guangke Chen, Zhe Zhao, Fu Song +3

Adversarial attacks have been expanded to speaker recognition (SR). However, existing attacks are often assessed using different SR models, recognition tasks and datasets, and only…

cs.CR2021★ 2 cited

Attack as Defense: Characterizing Adversarial Examples using Robustness

Zhe Zhao, Guangke Chen, Jingyi Wang +3

As a new programming paradigm, deep learning has expanded its application to many real-world problems. At the same time, deep learning based software are found to be vulnerable to…