33 citations · 88 across the 17 of their papers we have counts for
5 papers · 1 filter
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…