77 citations · 142 across the 15 of their papers we have counts for
15 papers
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…
Toward Evaluating Robustness of Reinforcement Learning with Adversarial Policy
Xiang Zheng, Xingjun Ma, Shengjie Wang +3
Reinforcement learning agents are susceptible to evasion attacks during deployment. In single-agent environments, these attacks can occur through imperceptible perturbations inject…
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…
FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons
Xuanqi Gao, Juan Zhai, Shiqing Ma +3
With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-reaching impacts on society, there are increasing concerns on their ethical perfo…
WaveFuzz: A Clean-Label Poisoning Attack to Protect Your Voice
Yunjie Ge, Qian Wang, Jingfeng Zhang +3
People are not always receptive to their voice data being collected and misused. Training the audio intelligence systems needs these data to build useful features, but the cost for…
Towards Benchmarking and Evaluating Deepfake Detection
Chenhao Lin, Jingyi Deng, Pengbin Hu +3
Deepfake detection automatically recognizes the manipulated medias through the analysis of the difference between manipulated and non-altered videos. It is natural to ask which are…
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…
CARTL: Cooperative Adversarially-Robust Transfer Learning
Dian Chen, Hongxin Hu, Qian Wang +4
Transfer learning eases the burden of training a well-performed model from scratch, especially when training data is scarce and computation power is limited. In deep learning, a ty…
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…
Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?
Kaidi Jin, Tianwei Zhang, Chao Shen +4
Deep Neural Networks are well known to be vulnerable to adversarial attacks and backdoor attacks, where minor modifications on the input are able to mislead the models to give wron…
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…