77 citations · 142 across the 15 of their papers we have counts for
9 papers · 1 filter
Zero-Query Adversarial Attack on Black-box Automatic Speech Recognition Systems
Zheng Fang, Tao Wang, Lingchen Zhao +6
In recent years, extensive research has been conducted on the vulnerability of ASR systems, revealing that black-box adversarial example attacks pose significant threats to real-wo…
Hijacking Attacks against Neural Networks by Analyzing Training Data
Yunjie Ge, Qian Wang, Huayang Huang +7
Backdoors and adversarial examples are the two primary threats currently faced by deep neural networks (DNNs). Both attacks attempt to hijack the model behaviors with unintended ou…
Amplifying Membership Exposure via Data Poisoning
Yufei Chen, Chao Shen, Yun Shen +2
As in-the-wild data are increasingly involved in the training stage, machine learning applications become more susceptible to data poisoning attacks. Such attacks typically lead to…
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information
Baolin Zheng, Peipei Jiang, Qian Wang +6
Adversarial attacks against commercial black-box speech platforms, including cloud speech APIs and voice control devices, have received little attention until recent years. The cur…
Property Inference Attacks Against GANs
Junhao Zhou, Yufei Chen, Chao Shen +1
While machine learning (ML) has made tremendous progress during the past decade, recent research has shown that ML models are vulnerable to various security and privacy attacks. So…
Securing Face Liveness Detection Using Unforgeable Lip Motion Patterns
Man Zhou, Qian Wang, Qi Li +5
Face authentication usually utilizes deep learning models to verify users with high recognition accuracy. However, face authentication systems are vulnerable to various attacks tha…
Teacher Model Fingerprinting Attacks Against Transfer Learning
Yufei Chen, Chao Shen, Cong Wang +1
Transfer learning has become a common solution to address training data scarcity in practice. It trains a specified student model by reusing or fine-tuning early layers of a well-t…
Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection
Lingchen Zhao, Shengshan Hu, Qian Wang +4
Collaborative learning allows multiple clients to train a joint model without sharing their data with each other. Each client performs training locally and then submits the model u…
VeriML: Enabling Integrity Assurances and Fair Payments for Machine Learning as a Service
Lingchen Zhao, Qian Wang, Cong Wang +5
Machine Learning as a Service (MLaaS) allows clients with limited resources to outsource their expensive ML tasks to powerful servers. Despite the huge benefits, current MLaaS solu…