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20112025
most citedInvisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization

43 citations · 131 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CR2025

Your Microphone Array Retains Your Identity: A Robust Voice Liveness Detection System for Smart Speakers

Yan Meng, Jiachun Li, Matthew Pillari +5

Though playing an essential role in smart home systems, smart speakers are vulnerable to voice spoofing attacks. Passive liveness detection, which utilizes only the collected audio…

cs.CR2022★ 3 cited

Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations

Zirui Peng, Shaofeng Li, Guoxing Chen +3

In this paper, we propose a novel and practical mechanism which enables the service provider to verify whether a suspect model is stolen from the victim model via model extraction…

cs.CR2021★ 1 cited

Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors

Ruoxi Sun, Minhui Xue, Gareth Tyson +6

Numerous open-source and commercial malware detectors are available. However, their efficacy is threatened by new adversarial attacks, whereby malware attempts to evade detection,…

cs.CR2021★ 15 cited

Dissecting Click Fraud Autonomy in the Wild

Tong Zhu, Yan Meng, Haotian Hu +3

Although the use of pay-per-click mechanisms stimulates the prosperity of the mobile advertisement network, fraudulent ad clicks result in huge financial losses for advertisers. Ex…

cs.CL2021★ 5 cited

Hidden Backdoors in Human-Centric Language Models

Shaofeng Li, Hui Liu, Tian Dong +4

Natural language processing (NLP) systems have been proven to be vulnerable to backdoor attacks, whereby hidden features (backdoors) are trained into a language model and may only…

cs.CR2019★ 43 cited

Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization

Shaofeng Li, Minhui Xue, Benjamin Zi Hao Zhao +2

Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks, where hidden features (patterns) trained to a normal model, which is only activated by some specific in…

cs.CR2018★ 31 cited

Differentially Private Data Generative Models

Qingrong Chen, Chong Xiang, Minhui Xue +4

Deep neural networks (DNNs) have recently been widely adopted in various applications, and such success is largely due to a combination of algorithmic breakthroughs, computation re…

cs.SI2017★ 7 cited

Smoke Screener or Straight Shooter: Detecting Elite Sybil Attacks in User-Review Social Networks

Haizhong Zheng, Minhui Xue, Hao Lu +4

Popular User-Review Social Networks (URSNs)---such as Dianping, Yelp, and Amazon---are often the targets of reputation attacks in which fake reviews are posted in order to boost or…

cs.CR2017★ 9 cited

Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach

Sen Chen, Minhui Xue, Lingling Fan +4

The evolution of mobile malware poses a serious threat to smartphone security. Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via pol…

cs.DC2014★ 2 cited

HVSTO: Efficient Privacy Preserving Hybrid Storage in Cloud Data Center

Mianxiong Dong, He Li, Kaoru Ota +1

In cloud data center, shared storage with good management is a main structure used for the storage of virtual machines (VM). In this paper, we proposed Hybrid VM storage (HVSTO), a…

cs.SI2013★ 15 cited

All Your Location are Belong to Us: Breaking Mobile Social Networks for Automated User Location Tracking

Muyuan Li, Haojin Zhu, Zhaoyu Gao +4

Many popular location-based social networks (LBSNs) support built-in location-based social discovery with hundreds of millions of users around the world. While user (near) realtime…

cs.NI2011

YouSense: Mitigating Entropy Selfishness in Distributed Collaborative Spectrum Sensing

Shuai Li, Haojin Zhu, Zhaoyu Gao +2

Collaborative spectrum sensing has been recognized as a promising approach to improve the sensing performance via exploiting the spatial diversity of the secondary users. In this s…