3 papers
cs.CR2026
Detecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning
Shenao Yan, Shimaa Ahmed, Shan Jin +4
Code generation large language models (LLMs) are increasingly integrated into modern software development workflows. Recent work has shown that these models are vulnerable to backd…
cs.CR2025
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Nima Naderloui, Shenao Yan, Binghui Wang +4
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning en…
cs.CV2024
GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation
Hanbin Hong, Shenao Yan, Shuya Feng +2
Active Learning (AL) represents a crucial methodology within machine learning, emphasizing the identification and utilization of the most informative samples for efficient model tr…