19 citations · 66 across the 11 of their papers we have counts for
12 papers
Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand
Junfeng Guo, Yiming Li, Lixu Wang +4
The prosperity of deep neural networks (DNNs) is largely benefited from open-source datasets, based on which users can evaluate and improve their methods. In this paper, we revisit…
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
Zexin Li, Bangjie Yin, Taiping Yao +4
A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i.e., inaccessible gradient and parameter information…
SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency
Junfeng Guo, Yiming Li, Xun Chen +3
Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries embed a hidden backdoor trigger during the training process for malicious prediction manipulation.…
PolicyCleanse: Backdoor Detection and Mitigation in Reinforcement Learning
Junfeng Guo, Ang Li, Cong Liu
While real-world applications of reinforcement learning are becoming popular, the security and robustness of RL systems are worthy of more attention and exploration. In particular,…
AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis
Junfeng Guo, Ang Li, Cong Liu
Deep neural networks (DNNs) are proved to be vulnerable against backdoor attacks. A backdoor is often embedded in the target DNNs through injecting a backdoor trigger into training…
Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition
Bangjie Yin, Wenxuan Wang, Taiping Yao +5
Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples. However, existing adversarial exampl…
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection
Xin Dong, Junfeng Guo, Ang Li +3
Various approaches have been proposed for out-of-distribution (OOD) detection by augmenting models, input examples, training sets, and optimization objectives. Deviating from exist…
PredCoin: Defense against Query-based Hard-label Attack
Junfeng Guo, Yaswanth Yadlapalli, Thiele Lothar +2
Many adversarial attacks and defenses have recently been proposed for Deep Neural Networks (DNNs). While most of them are in the white-box setting, which is impractical, a new clas…
LINTS^RT: A Learning-driven Testbed for Intelligent Scheduling in Embedded Systems
Zelun Kong, Yaswanth Yadlapalli, Soroush Bateni +2
Due to the increasing complexity seen in both workloads and hardware resources in state-of-the-art embedded systems, developing efficient real-time schedulers and the corresponding…
Deep Partial Updating: Towards Communication Efficient Updating for On-device Inference
Zhongnan Qu, Cong Liu, Lothar Thiele
Emerging edge intelligence applications require the server to retrain and update deep neural networks deployed on remote edge nodes to leverage newly collected data samples. Unfort…
PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks
Junfeng Guo, Ting Wang, Cong Liu
The black-box nature of deep neural networks (DNNs) facilitates attackers to manipulate the behavior of DNN through data poisoning. Being able to detect and mitigate poisoning atta…
PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving
Zelun Kong, Junfeng Guo, Ang Li +1
Although Deep neural networks (DNNs) are being pervasively used in vision-based autonomous driving systems, they are found vulnerable to adversarial attacks where small-magnitude p…