591 citations · 1k across the 37 of their papers we have counts for
37 papers
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…
Copyright Protection and Accountability of Generative AI:Attack, Watermarking and Attribution
Haonan Zhong, Jiamin Chang, Ziyue Yang +4
Generative AI (e.g., Generative Adversarial Networks - GANs) has become increasingly popular in recent years. However, Generative AI introduces significant concerns regarding the p…
M^4I: Multi-modal Models Membership Inference
Pingyi Hu, Zihan Wang, Ruoxi Sun +2
With the development of machine learning techniques, the attention of research has been moved from single-modal learning to multi-modal learning, as real-world data exist in the fo…
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…
StyleFool: Fooling Video Classification Systems via Style Transfer
Yuxin Cao, Xi Xiao, Ruoxi Sun +3
Video classification systems are vulnerable to adversarial attacks, which can create severe security problems in video verification. Current black-box attacks need a large number o…
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…
PPA: Preference Profiling Attack Against Federated Learning
Chunyi Zhou, Yansong Gao, Anmin Fu +5
Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require d…
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,…
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems
Bao Gia Doan, Minhui Xue, Shiqing Ma +2
Deep neural networks are vulnerable to attacks from adversarial inputs and, more recently, Trojans to misguide or hijack the model's decision. We expose the existence of an intrigu…
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…
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Zihan Wang, Olivia Byrnes, Hu Wang +5
The advancement of secure communication and identity verification fields has significantly increased through the use of deep learning techniques for data hiding. By embedding infor…
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…
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…
Explainability-based Backdoor Attacks Against Graph Neural Networks
Jing Xu, Minhui, Xue +1
Backdoor attacks represent a serious threat to neural network models. A backdoored model will misclassify the trigger-embedded inputs into an attacker-chosen target label while per…
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…
Delayed Rewards Calibration via Reward Empirical Sufficiency
Yixuan Liu, Hu Wang, Xiaowei Wang +3
Appropriate credit assignment for delay rewards is a fundamental challenge for reinforcement learning. To tackle this problem, we introduce a delay reward calibration paradigm insp…
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…