110 citations · 226 across the 20 of their papers we have counts for
8 papers · 1 filter
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Yanli Li, Zhongliang Guo, Nan Yang +3
Federated Learning (FL) offers innovative solutions for privacy-preserving collaborative machine learning (ML). Despite its promising potential, FL is vulnerable to various attacks…
Fairpriori: Improving Biased Subgroup Discovery for Deep Neural Network Fairness
Kacy Zhou, Jiawen Wen, Nan Yang +3
While deep learning has become a core functional module of most software systems, concerns regarding the fairness of ML predictions have emerged as a significant issue that affects…
On Security Weaknesses and Vulnerabilities in Deep Learning Systems
Zhongzheng Lai, Huaming Chen, Ruoxi Sun +3
The security guarantee of AI-enabled software systems (particularly using deep learning techniques as a functional core) is pivotal against the adversarial attacks exploiting softw…
DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks
Zhiyu Zhu, Jiayu Zhang, Xinyi Wang +2
Despite the exceptional performance of deep neural networks (DNNs) across different domains, they are vulnerable to adversarial samples, in particular for tasks related to computer…
Perturbation Effects on Robustness and Individual Fairness
Xuran Li, Hao Xue, Peng Wu +4
Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.…
Benchmarking Transferable Adversarial Attacks
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu +1
The robustness of deep learning models against adversarial attacks remains a pivotal concern. This study presents, for the first time, an exhaustive review of the transferability a…
On the Challenges of Fuzzing Techniques via Large Language Models
Linghan Huang, Peizhou Zhao, Huaming Chen +1
In the modern era where software plays a pivotal role, software security and vulnerability analysis are essential for secure software development. Fuzzing test, as an efficient and…
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
Zhiyu Zhu, Huaming Chen, Xinyi Wang +5
Adversarial generative models, such as Generative Adversarial Networks (GANs), are widely applied for generating various types of data, i.e., images, text, and audio. Accordingly,…