526 citations · 989 across the 18 of their papers we have counts for
20 papers
Any-Depth Alignment: Unlocking Innate Safety Alignment of LLMs to Any-Depth
Jiawei Zhang, Andrew Estornell, David D. Baek +2
Large Language Models (LLMs) exhibit strong but shallow alignment: they directly refuse harmful queries when a refusal is expected at the very start of an assistant turn, yet this…
KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data
Andy Zhou, Xiaojun Xu, Ramesh Raghunathan +4
Graph-based anomaly detection is pivotal in diverse security applications, such as fraud detection in transaction networks and intrusion detection for network traffic. Standard app…
Game of Trojans: Adaptive Adversaries Against Output-based Trojaned-Model Detectors
Dinuka Sahabandu, Xiaojun Xu, Arezoo Rajabi +4
We propose and analyze an adaptive adversary that can retrain a Trojaned DNN and is also aware of SOTA output-based Trojaned model detectors. We show that such an adversary can ens…
Effective and Efficient Federated Tree Learning on Hybrid Data
Qinbin Li, Chulin Xie, Xiaojun Xu +5
Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data. However, m…
EDoG: Adversarial Edge Detection For Graph Neural Networks
Xiaojun Xu, Yue Yu, Hanzhang Wang +3
Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are…
LOT: Layer-wise Orthogonal Training on Improving Certified Robustness
Xiaojun Xu, Linyi Li, Bo Li
Recent studies show that training deep neural networks (DNNs) with Lipschitz constraints are able to enhance adversarial robustness and other model properties such as stability. In…
UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks
Xiaoyuan Liu, Tianneng Shi, Chulin Xie +9
Federated Learning (FL) has become a practical and widely adopted distributed learning paradigm. However, the lack of a comprehensive and standardized solution covering diverse use…
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization
Xiaojun Xu, Jacky Yibo Zhang, Evelyn Ma +3
Machine learning (ML) robustness and domain generalization are fundamentally correlated: they essentially concern data distribution shifts under adversarial and natural settings, r…
On the Certified Robustness for Ensemble Models and Beyond
Zhuolin Yang, Linyi Li, Xiaojun Xu +3
Recent studies show that deep neural networks (DNN) are vulnerable to adversarial examples, which aim to mislead DNNs by adding perturbations with small magnitude. To defend agains…
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness
Zhuolin Yang, Linyi Li, Xiaojun Xu +6
Adversarial Transferability is an intriguing property - adversarial perturbation crafted against one model is also effective against another model, while these models are from diff…
Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks
Huichen Li, Linyi Li, Xiaojun Xu +3
Gradient estimation and vector space projection have been studied as two distinct topics. We aim to bridge the gap between the two by investigating how to efficiently estimate grad…
QEBA: Query-Efficient Boundary-Based Blackbox Attack
Huichen Li, Xiaojun Xu, Xiaolu Zhang +2
Machine learning (ML), especially deep neural networks (DNNs) have been widely used in various applications, including several safety-critical ones (e.g. autonomous driving). As a…
RAB: Provable Robustness Against Backdoor Attacks
Maurice Weber, Xiaojun Xu, Bojan Karlaš +2
Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there hav…
TSS: Transformation-Specific Smoothing for Robustness Certification
Linyi Li, Maurice Weber, Xiaojun Xu +5
As machine learning (ML) systems become pervasive, safeguarding their security is critical. However, recently it has been demonstrated that motivated adversaries are able to mislea…
Detecting AI Trojans Using Meta Neural Analysis
Xiaojun Xu, Qi Wang, Huichen Li +3
In machine learning Trojan attacks, an adversary trains a corrupted model that obtains good performance on normal data but behaves maliciously on data samples with certain trigger…
A Machine Learning Approach To Prevent Malicious Calls Over Telephony Networks
Huichen Li, Xiaojun Xu, Chang Liu +6
Malicious calls, i.e., telephony spams and scams, have been a long-standing challenging issue that causes billions of dollars of annual financial loss worldwide. This work presents…
A Neural Stochastic Volatility Model
Rui Luo, Weinan Zhang, Xiaojun Xu +1
In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation o…
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
Xiaojun Xu, Chang Liu, Dawn Song
Synthesizing SQL queries from natural language is a long-standing open problem and has been attracting considerable interest recently. Toward solving the problem, the de facto appr…
Fooling Vision and Language Models Despite Localization and Attention Mechanism
Xiaojun Xu, Xinyun Chen, Chang Liu +3
Adversarial attacks are known to succeed on classifiers, but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial ex…
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection
Xiaojun Xu, Chang Liu, Qian Feng +3
The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many securi…