33 citations · 45 across the 5 of their papers we have counts for
6 papers
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…
Extracting Cloud-based Model with Prior Knowledge
Shiqian Zhao, Kangjie Chen, Meng Hao +4
Machine Learning-as-a-Service, a pay-as-you-go business pattern, is widely accepted by third-party users and developers. However, the open inference APIs may be utilized by malicio…
ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks
Xiaoxuan Lou, Guowen Xu, Kangjie Chen +3
Multiplication-less neural networks significantly reduce the time and energy cost on the hardware platform, as the compute-intensive multiplications are replaced with lightweight b…
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
Kangjie Chen, Yuxian Meng, Xiaofei Sun +4
Pre-trained Natural Language Processing (NLP) models can be easily adapted to a variety of downstream language tasks. This significantly accelerates the development of language mod…
Stealing Deep Reinforcement Learning Models for Fun and Profit
Kangjie Chen, Shangwei Guo, Tianwei Zhang +2
This paper presents the first model extraction attack against Deep Reinforcement Learning (DRL), which enables an external adversary to precisely recover a black-box DRL model only…
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…