180 citations · 951 across the 62 of their papers we have counts for
14 papers · 1 filter
Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis
Xiyue Zhang, Xiaoyong Xue, Xiaoning Du +3
Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning fro…
Large Language Model Supply Chain: Open Problems From the Security Perspective
Qiang Hu, Xiaofei Xie, Sen Chen +1
Large Language Model (LLM) is changing the software development paradigm and has gained huge attention from both academia and industry. Researchers and developers collaboratively e…
GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis
Yuqiang Sun, Daoyuan Wu, Yue Xue +5
Smart contracts are prone to various vulnerabilities, leading to substantial financial losses over time. Current analysis tools mainly target vulnerabilities with fixed control or…
How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation
Cen Zhang, Yaowen Zheng, Mingqiang Bai +6
LLM-based (Large Language Model) fuzz driver generation is a promising research area. Unlike traditional program analysis-based method, this text-based approach is more general and…
Multi-target Backdoor Attacks for Code Pre-trained Models
Yanzhou Li, Shangqing Liu, Kangjie Chen +3
Backdoor attacks for neural code models have gained considerable attention due to the advancement of code intelligence. However, most existing works insert triggers into task-speci…
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search
Jie Zhang, Wei Ma, Qiang Hu +4
Existing methods for generating adversarial code examples face several challenges: limted availability of substitute variables, high verification costs for these substitutes, and t…
Decompiling x86 Deep Neural Network Executables
Zhibo Liu, Yuanyuan Yuan, Shuai Wang +2
Due to their widespread use on heterogeneous hardware devices, deep learning (DL) models are compiled into executables by DL compilers to fully leverage low-level hardware primitiv…
Enhancing Security Patch Identification by Capturing Structures in Commits
Bozhi Wu, Shangqing Liu, Ruitao Feng +3
With the rapid increasing number of open source software (OSS), the majority of the software vulnerabilities in the open source components are fixed silently, which leads to the de…
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems
Haoliang Li, Yufei Wang, Xiaofei Xie +5
Deep neural networks (DNN) have shown great success in many computer vision applications. However, they are also known to be susceptible to backdoor attacks. When conducting backdo…
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis
Ming Fan, Wenying Wei, Xiaofei Xie +3
With the rapid growth of Android malware, many machine learning-based malware analysis approaches are proposed to mitigate the severe phenomenon. However, such classifiers are opaq…
Generating Adversarial Examples with Controllable Non-transferability
Renzhi Wang, Tianwei Zhang, Xiaofei Xie +4
Adversarial attacks against Deep Neural Networks have been widely studied. One significant feature that makes such attacks particularly powerful is transferability, where the adver…
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning
Jianwen Sun, Tianwei Zhang, Xiaofei Xie +4
Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasib…
A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices
Ruitao Feng, Sen Chen, Xiaofei Xie +3
Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection fo…
FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces
Run Wang, Felix Juefei-Xu, Lei Ma +4
In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis. They are widely adopted in synthesizing facial imag…