110 citations · 226 across the 20 of their papers we have counts for
21 papers
FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment
Xitong Zeng, Zhaoge Bi, Yitian Yang +2
Machine learning models are widely used in financial fraud and credit-risk detection, yet their adversarial robustness remains difficult to evaluate because financial tabular data…
When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech
Qingwen Zeng, Zhenghao Zhao, Yitian Yang +6
Artificial intelligence is now embedded as a primary decision engine in continuously operated financial AI pipelines spanning training and updating, deployment and inference, and o…
Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future
Qingwen Zeng, Hanlin Xu, Nanjun Xu +5
Carbon emissions significantly contribute to climate change, and carbon credits have emerged as a key tool for mitigating environmental damage and helping organizations manage thei…
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,…
FairCompass: Operationalising Fairness in Machine Learning
Jessica Liu, Huaming Chen, Jun Shen +1
As artificial intelligence (AI) increasingly becomes an integral part of our societal and individual activities, there is a growing imperative to develop responsible AI solutions.…
MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks
Zhiyu Zhu, Huaming Chen, Jiayu Zhang +5
To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each i…
Code Ownership in Open-Source AI Software Security
Jiawen Wen, Dong Yuan, Lei Ma +1
As open-source AI software projects become an integral component in the AI software development, it is critical to develop a novel methods to ensure and measure the security of the…
Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios
Yanli Li, Huaming Chen, Wei Bao +2
Federated Learning (FL) is a promising technology that enables multiple actors to build a joint model without sharing their raw data. The distributed nature makes FL vulnerable to…
DANAA: Towards transferable attacks with double adversarial neuron attribution
Zhibo Jin, Zhiyu Zhu, Xinyi Wang +3
While deep neural networks have excellent results in many fields, they are susceptible to interference from attacking samples resulting in erroneous judgments. Feature-level attack…
Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models
Yuheng Huang, Jiayang Song, Zhijie Wang +4
The recent performance leap of Large Language Models (LLMs) opens up new opportunities across numerous industrial applications and domains. However, erroneous generations, such as…
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads
Yu Zhang, Huaming Chen, Wei Bao +3
With the rapid development of deep learning, object detection and tracking play a vital role in today's society. Being able to identify and track all the pedestrians in the dense c…
Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges
Huaming Chen, M. Ali Babar
The rapid development of Machine Learning (ML) has demonstrated superior performance in many areas, such as computer vision, video and speech recognition. It has now been increasin…
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…
A Survey on Data-driven Software Vulnerability Assessment and Prioritization
Triet H. M. Le, Huaming Chen, M. Ali Babar
Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems. Given the limited resources in practice, SV assessment…