333 citations · 649 across the 28 of their papers we have counts for
7 papers · 1 filter
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…
TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors
Ren Pang, Zheng Zhang, Xiangshan Gao +5
Neural backdoors represent one primary threat to the security of deep learning systems. The intensive research has produced a plethora of backdoor attacks/defenses, resulting in a…
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…
Graph Backdoor
Zhaohan Xi, Ren Pang, Shouling Ji +1
One intriguing property of deep neural networks (DNNs) is their inherent vulnerability to backdoor attacks -- a trojan model responds to trigger-embedded inputs in a highly predict…
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…
Where Classification Fails, Interpretation Rises
Chanh Nguyen, Georgi Georgiev, Yujie Ji +1
An intriguing property of deep neural networks is their inherent vulnerability to adversarial inputs, which significantly hinders their application in security-critical domains. Mo…