6 citations · 23 across the 11 of their papers we have counts for
12 papers
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…
Training Verification-Friendly Neural Networks via Neuron Behavior Consistency
Zongxin Liu, Zhe Zhao, Fu Song +4
Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel m…
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…
QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks
Yedi Zhang, Zhe Zhao, Fu Song +3
Deep learning has become a promising programming paradigm in software development, owing to its surprising performance in solving many challenging tasks. Deep neural networks (DNNs…
Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition
Guangke Chen, Zhe Zhao, Fu Song +4
Speaker recognition systems (SRSs) have recently been shown to be vulnerable to adversarial attacks, raising significant security concerns. In this work, we systematically investig…
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems
Guangke Chen, Zhe Zhao, Fu Song +3
Recent work has illuminated the vulnerability of speaker recognition systems (SRSs) against adversarial attacks, raising significant security concerns in deploying SRSs. However, t…
Adversarial Attacks on ML Defense Models Competition
Yinpeng Dong, Qi-An Fu, Xiao Yang +25
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.…
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…
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…
BDD4BNN: A BDD-based Quantitative Analysis Framework for Binarized Neural Networks
Yedi Zhang, Zhe Zhao, Guangke Chen +2
Verifying and explaining the behavior of neural networks is becoming increasingly important, especially when they are deployed in safety-critical applications. In this paper, we st…
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
Guangke Chen, Sen Chen, Lingling Fan +4
Speaker recognition (SR) is widely used in our daily life as a biometric authentication or identification mechanism. The popularity of SR brings in serious security concerns, as de…
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain
Lei Bu, Yuchao Duan, Fu Song +1
Numerous methods for crafting adversarial examples were proposed recently with high success rate. Since most existing machine learning based classifiers normalize images into some…