591 citations · 1k across the 37 of their papers we have counts for
21 papers · 1 filter
The "code'' of Ethics:A Holistic Audit of AI Code Generators
Wanlun Ma, Yiliao Song, Minhui Xue +2
AI-powered programming language generation (PLG) models have gained increasing attention due to their ability to generate source code of programs in a few seconds with a plain prog…
Not Seen, Not Heard in the Digital World! Measuring Privacy Practices in Children's Apps
Ruoxi Sun, Minhui Xue, Gareth Tyson +3
The digital age has brought a world of opportunity to children. Connectivity can be a game-changer for some of the world's most marginalized children. However, while legislatures a…
The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices
Wanlun Ma, Derui Wang, Ruoxi Sun +3
Deep Neural Networks (DNNs) are susceptible to backdoor attacks during training. The model corrupted in this way functions normally, but when triggered by certain patterns in the i…
PublicCheck: Public Integrity Verification for Services of Run-time Deep Models
Shuo Wang, Sharif Abuadbba, Sidharth Agarwal +6
Existing integrity verification approaches for deep models are designed for private verification (i.e., assuming the service provider is honest, with white-box access to model para…
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…
Statically Detecting Adversarial Malware through Randomised Chaining
Matthew Crawford, Wei Wang, Ruoxi Sun +1
With the rapid growth of malware attacks, more antivirus developers consider deploying machine learning technologies into their productions. Researchers and developers published va…
Dissecting Malware in the Wild
Hamish Spencer, Wei Wang, Ruoxi Sun +1
With the increasingly rapid development of new malicious computer software by bad faith actors, both commercial and research-oriented antivirus detectors have come to make greater…
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,…
Quantization Backdoors to Deep Learning Commercial Frameworks
Hua Ma, Huming Qiu, Yansong Gao +7
Currently, there is a burgeoning demand for deploying deep learning (DL) models on ubiquitous edge Internet of Things (IoT) devices attributed to their low latency and high privacy…
TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing
Aoting Hu, Renjie Xie, Zhigang Lu +2
Generative Adversarial Networks (GAN)-synthesized table publishing lets people privately learn insights without access to the private table. However, existing studies on Membership…
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…
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models
Liuqiao Chen, Hu Wang, Benjamin Zi Hao Zhao +2
Deep Neural Networks have achieved unprecedented success in the field of face recognition such that any individual can crawl the data of others from the Internet without their expl…
Deep Learning Backdoors
Shaofeng Li, Shiqing Ma, Minhui Xue +1
Intuitively, a backdoor attack against Deep Neural Networks (DNNs) is to inject hidden malicious behaviors into DNNs such that the backdoor model behaves legitimately for benign in…
With Great Dispersion Comes Greater Resilience: Efficient Poisoning Attacks and Defenses for Linear Regression Models
Jialin Wen, Benjamin Zi Hao Zhao, Minhui Xue +2
With the rise of third parties in the machine learning pipeline, the service provider in "Machine Learning as a Service" (MLaaS), or external data contributors in online learning,…
An Empirical Assessment of Global COVID-19 Contact Tracing Applications
Ruoxi Sun, Wei Wang, Minhui Xue +3
The rapid spread of COVID-19 has made manual contact tracing difficult. Thus, various public health authorities have experimented with automatic contact tracing using mobile applic…
Quality Assessment of Online Automated Privacy Policy Generators: An Empirical Study
Ruoxi Sun, Minhui Xue
Online Automated Privacy Policy Generators (APPGs) are tools used by app developers to quickly create app privacy policies which are required by privacy regulations to be incorpora…
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…
The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services
Yuantian Miao, Minhui Xue, Chao Chen +5
With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper,…
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…
An Empirical Assessment of Security Risks of Global Android Banking Apps
Sen Chen, Lingling Fan, Guozhu Meng +5
Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial los…
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…