553 citations · 1.6k across the 70 of their papers we have counts for
70 papers
RobustMQ: Benchmarking Robustness of Quantized Models
Yisong Xiao, Aishan Liu, Tianyuan Zhang +3
Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities wh…
Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks
Jun Guo, Aishan Liu, Xingyu Zheng +4
Despite the broad application of Machine Learning models as a Service (MLaaS), they are vulnerable to model stealing attacks. These attacks can replicate the model functionality by…
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
Yan Wang, Yuhang Li, Ruihao Gong +8
Extensive studies have shown that deep learning models are vulnerable to adversarial and natural noises, yet little is known about model robustness on noises caused by different sy…
One stone, two birds: A lightweight multidimensional learned index with cardinality support
Yingze Li, Hongzhi Wang, Xianglong Liu
Innovative learning based structures have recently been proposed to tackle index and cardinality estimation tasks, specifically learned indexes and data driven cardinality estimato…
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
Simin Li, Shuing Zhang, Gujun Chen +6
Physical world adversarial attack is a highly practical and threatening attack, which fools real world deep learning systems by generating conspicuous and maliciously crafted real…
Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing
Yisong Xiao, Aishan Liu, Tianlin Li +1
Machine learning (ML) systems have achieved remarkable performance across a wide area of applications. However, they frequently exhibit unfair behaviors in sensitive application do…
Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game
Simin Li, Jun Guo, Jingqiao Xiu +6
In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case acti…
Towards Accurate Post-Training Quantization for Vision Transformer
Yifu Ding, Haotong Qin, Qinghua Yan +4
Vision transformer emerges as a potential architecture for vision tasks. However, the intense computation and non-negligible delay hinder its application in the real world. As a wi…
TS-Cabinet: Hierarchical Storage for Cloud-Edge-End Time-series Database
Shuangshuang Cui, Hongzhi Wang, Xianglong Liu +2
Hierarchical data storage is crucial for cloud-edge-end time-series database. Efficient hierarchical storage will directly reduce the storage space of local databases at each side…
X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection
Aishan Liu, Jun Guo, Jiakai Wang +6
Adversarial attacks are valuable for evaluating the robustness of deep learning models. Existing attacks are primarily conducted on the visible light spectrum (e.g., pixel-wise tex…
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
Simin Li, Jun Guo, Jingqiao Xiu +8
This study probes the vulnerabilities of cooperative multi-agent reinforcement learning (c-MARL) under adversarial attacks, a critical determinant of c-MARL's worst-case performanc…
BiBench: Benchmarking and Analyzing Network Binarization
Haotong Qin, Mingyuan Zhang, Yifu Ding +5
Network binarization emerges as one of the most promising compression approaches offering extraordinary computation and memory savings by minimizing the bit-width. However, recent…
Phonemic Adversarial Attack against Audio Recognition in Real World
Jiakai Wang, Zhendong Chen, Zixin Yin +2
Recently, adversarial attacks for audio recognition have attracted much attention. However, most of the existing studies mainly rely on the coarse-grain audio features at the insta…
BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance
Haotong Qin, Xudong Ma, Yifu Ding +6
Deep neural networks, such as the Deep-FSMN, have been widely studied for keyword spotting (KWS) applications while suffering expensive computation and storage. Therefore, network…
Improving Robust Fairness via Balance Adversarial Training
Chunyu Sun, Chenye Xu, Chengyuan Yao +5
Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes, known as the…
Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies
Xingrun Xing, Yangguang Li, Wei Li +6
Existing Binary Neural Networks (BNNs) mainly operate on local convolutions with binarization function. However, such simple bit operations lack the ability of modeling contextual…
Exploring the Relationship between Architecture and Adversarially Robust Generalization
Aishan Liu, Shiyu Tang, Siyuan Liang +4
Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization…
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
Xiuying Wei, Yunchen Zhang, Xiangguo Zhang +5
Transformer architecture has become the fundamental element of the widespread natural language processing~(NLP) models. With the trends of large NLP models, the increasing memory a…
Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection
Simin Li, Huangxinxin Xu, Jiakai Wang +4
Billions of people are sharing their daily life images on social media every day. However, their biometric information (e.g., fingerprint) could be easily stolen from these images.…
Frequency Domain Model Augmentation for Adversarial Attack
Yuyang Long, Qilong Zhang, Boheng Zeng +4
For black-box attacks, the gap between the substitute model and the victim model is usually large, which manifests as a weak attack performance. Motivated by the observation that t…
Defensive Patches for Robust Recognition in the Physical World
Jiakai Wang, Zixin Yin, Pengfei Hu +5
To operate in real-world high-stakes environments, deep learning systems have to endure noises that have been continuously thwarting their robustness. Data-end defense, which impro…
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack
Ye Liu, Yaya Cheng, Lianli Gao +3
Defense models against adversarial attacks have grown significantly, but the lack of practical evaluation methods has hindered progress. Evaluation can be defined as looking for de…
Delving into the Estimation Shift of Batch Normalization in a Network
Lei Huang, Yi Zhou, Tian Wang +2
Batch normalization (BN) is a milestone technique in deep learning. It normalizes the activation using mini-batch statistics during training but the estimated population statistics…
BiBERT: Accurate Fully Binarized BERT
Haotong Qin, Yifu Ding, Mingyuan Zhang +5
The large pre-trained BERT has achieved remarkable performance on Natural Language Processing (NLP) tasks but is also computation and memory expensive. As one of the powerful compr…