17 citations · 58 across the 17 of their papers we have counts for
17 papers
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling
Kaiyuan Zhang, Siyuan Cheng, Guangyu Shen +5
Federated learning collaboratively trains a neural network on a global server, where each local client receives the current global model weights and sends back parameter updates (g…
UNIT: Backdoor Mitigation via Automated Neural Distribution Tightening
Siyuan Cheng, Guangyu Shen, Kaiyuan Zhang +5
Deep neural networks (DNNs) have demonstrated effectiveness in various fields. However, DNNs are vulnerable to backdoor attacks, which inject a unique pattern, called trigger, into…
LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning
Siyuan Cheng, Guanhong Tao, Yingqi Liu +7
Backdoor attack poses a significant security threat to Deep Learning applications. Existing attacks are often not evasive to established backdoor detection techniques. This suscept…
Opening A Pandora's Box: Things You Should Know in the Era of Custom GPTs
Guanhong Tao, Siyuan Cheng, Zhuo Zhang +3
The emergence of large language models (LLMs) has significantly accelerated the development of a wide range of applications across various fields. There is a growing trend in the c…
Make Them Spill the Beans! Coercive Knowledge Extraction from (Production) LLMs
Zhuo Zhang, Guangyu Shen, Guanhong Tao +2
Large Language Models (LLMs) are now widely used in various applications, making it crucial to align their ethical standards with human values. However, recent jail-breaking method…
Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift
Shengwei An, Sheng-Yen Chou, Kaiyuan Zhang +8
Diffusion models (DM) have become state-of-the-art generative models because of their capability to generate high-quality images from noises without adversarial training. However,…
PEM: Representing Binary Program Semantics for Similarity Analysis via a Probabilistic Execution Model
Xiangzhe Xu, Zhou Xuan, Shiwei Feng +7
Binary similarity analysis determines if two binary executables are from the same source program. Existing techniques leverage static and dynamic program features and may utilize a…
POSIT: Promotion of Semantic Item Tail via Adversarial Learning
Qiuling Xu, Pannaga Shivaswamy, Xiangyu Zhang
In many recommendations, a handful of popular items (e.g., movies / television shows, news, etc.) can be dominant in recommendations for many users. However, we know that in a larg…
ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP
Lu Yan, Zhuo Zhang, Guanhong Tao +4
Backdoor attacks have emerged as a prominent threat to natural language processing (NLP) models, where the presence of specific triggers in the input can lead poisoned models to mi…
Backdooring Neural Code Search
Weisong Sun, Yuchen Chen, Guanhong Tao +4
Reusing off-the-shelf code snippets from online repositories is a common practice, which significantly enhances the productivity of software developers. To find desired code snippe…
Detecting Backdoors in Pre-trained Encoders
Shiwei Feng, Guanhong Tao, Siyuan Cheng +6
Self-supervised learning in computer vision trains on unlabeled data, such as images or (image, text) pairs, to obtain an image encoder that learns high-quality embeddings for inpu…
BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense
Siyuan Cheng, Guanhong Tao, Yingqi Liu +8
Deep Learning backdoor attacks have a threat model similar to traditional cyber attacks. Attack forensics, a critical counter-measure for traditional cyber attacks, is hence of imp…
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks
Zhiyuan Cheng, James Liang, Guanhong Tao +2
Monocular Depth Estimation (MDE) is a critical component in applications such as autonomous driving. There are various attacks against MDE networks. These attacks, especially the p…
Backdoor Vulnerabilities in Normally Trained Deep Learning Models
Guanhong Tao, Zhenting Wang, Siyuan Cheng +7
We conduct a systematic study of backdoor vulnerabilities in normally trained Deep Learning models. They are as dangerous as backdoors injected by data poisoning because both can b…
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
Kaiyuan Zhang, Guanhong Tao, Qiuling Xu +8
Federated Learning (FL) is a distributed learning paradigm that enables different parties to train a model together for high quality and strong privacy protection. In this scenario…
DECK: Model Hardening for Defending Pervasive Backdoors
Guanhong Tao, Yingqi Liu, Siyuan Cheng +5
Pervasive backdoors are triggered by dynamic and pervasive input perturbations. They can be intentionally injected by attackers or naturally exist in normally trained models. They…
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack
Qiuling Xu, Guanhong Tao, Xiangyu Zhang
We propose a novel technique that can generate natural-looking adversarial examples by bounding the variations induced for internal activation values in some deep layer(s), through…