4 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
"No Matter What You Do": Purifying GNN Models via Backdoor Unlearning
Jiale Zhang, Chengcheng Zhu, Bosen Rao +5
Recent studies have exposed that GNNs are vulnerable to several adversarial attacks, among which backdoor attack is one of the toughest. Similar to Deep Neural Networks (DNNs), bac…
DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks
Hao Sui, Bing Chen, Jiale Zhang +4
Recent studies have revealed that GNNs are highly susceptible to multiple adversarial attacks. Among these, graph backdoor attacks pose one of the most prominent threats, where att…
Beyond Dataset Watermarking: Model-Level Copyright Protection for Code Summarization Models
Jiale Zhang, Haoxuan Li, Di Wu +3
Code Summarization Model (CSM) has been widely used in code production, such as online and web programming for PHP and Javascript. CSMs are essential tools in code production, enha…
Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning
Ye Li, Yanchao Zhao, Chengcheng Zhu +1
Federated Learning (FL), a privacy-preserving decentralized machine learning framework, has been shown to be vulnerable to backdoor attacks. Current research primarily focuses on t…
A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research
Sicong Cao, Xiaobing Sun, Ratnadira Widyasari +8
The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment across…