3 papers
cs.SE2026
Shapley-Guided Neural Repair Approach via Derivative-Free Optimization
Xinyu Sun, Wanwei Liu, Haoang Chi +7
DNNs are susceptible to defects like backdoors, adversarial attacks, and unfairness, undermining their reliability. Existing approaches mainly involve retraining, optimization, con…
cs.SE2024
FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection
Jialuo Chen, Jingyi Wang, Xiyue Zhang +4
Due to the vast testing space, the increasing demand for effective and efficient testing of deep neural networks (DNNs) has led to the development of various DNN test case prioriti…
cs.CR2024
Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection
Xiaokun Luan, Xiyue Zhang, Jingyi Wang +1
Model reuse techniques can reduce the resource requirements for training high-performance deep neural networks (DNNs) by leveraging existing models. However, unauthorized reuse and…