33 citations · 88 across the 17 of their papers we have counts for
18 papers
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…
CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models
Zhensu Sun, Xiaoning Du, Fu Song +1
Code datasets are of immense value for training neural-network-based code completion models, where companies or organizations have made substantial investments to establish and pro…
A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks
Weijie Shao, Yuyang Gao, Fu Song +3
Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing cli…
An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks
Ye Tao, Wanwei Liu, Fu Song +3
Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage cos…
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…
QEBVerif: Quantization Error Bound Verification of Neural Networks
Yedi Zhang, Fu Song, Jun Sun
To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge devices, quantization is widely regarded as one promising technique. It reduces the resourc…
Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems
Zhensu Sun, Xiaoning Du, Fu Song +4
Currently, large pre-trained language models are widely applied in neural code completion systems. Though large code models significantly outperform their smaller counterparts, aro…
Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks
Jiaxiang Liu, Yunhan Xing, Xiaomu Shi +3
As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical dom…
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…
CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning
Zhensu Sun, Xiaoning Du, Fu Song +2
Github Copilot, trained on billions of lines of public code, has recently become the buzzword in the computer science research and practice community. Although it is designed to he…
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…
Advanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifiers
Yusi Lei, Sen Chen, Lingling Fan +2
Machine learning (ML) based approaches have been the mainstream solution for anti-phishing detection. When they are deployed on the client-side, ML-based classifiers are vulnerable…
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…