12 citations · 17 across the 10 of their papers we have counts for
6 papers · 1 filter
Model Extraction Attacks Revisited
Jiacheng Liang, Ren Pang, Changjiang Li +1
Model extraction (ME) attacks represent one major threat to Machine-Learning-as-a-Service (MLaaS) platforms by ``stealing'' the functionality of confidential machine-learning model…
Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
Zhaohan Xi, Tianyu Du, Changjiang Li +5
Pre-trained language models (PLMs) have demonstrated remarkable performance as few-shot learners. However, their security risks under such settings are largely unexplored. In this…
On the Security Risks of AutoML
Ren Pang, Zhaohan Xi, Shouling Ji +2
Neural Architecture Search (NAS) represents an emerging machine learning (ML) paradigm that automatically searches for models tailored to given tasks, which greatly simplifies the…
i-Algebra: Towards Interactive Interpretability of Deep Neural Networks
Xinyang Zhang, Ren Pang, Shouling Ji +2
Providing explanations for deep neural networks (DNNs) is essential for their use in domains wherein the interpretability of decisions is a critical prerequisite. Despite the pleth…
AdvMind: Inferring Adversary Intent of Black-Box Attacks
Ren Pang, Xinyang Zhang, Shouling Ji +2
Deep neural networks (DNNs) are inherently susceptible to adversarial attacks even under black-box settings, in which the adversary only has query access to the target models. In p…
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
Ren Pang, Hua Shen, Xinyang Zhang +5
Despite their tremendous success in a range of domains, deep learning systems are inherently susceptible to two types of manipulations: adversarial inputs -- maliciously crafted sa…