133 citations · 465 across the 67 of their papers we have counts for
47 papers · 1 filter
JITterFlip: Uncovering Fault Attack Surfaces in JIT-Compiled LLM Serving
Tairui Wang, Zhi Zhang, Yansong Gao +3
LLMs are widely deployed through cloud-hosted inference services, where Just-in-Time (JIT) compilation is used to reduce recurring framework and GPU-launch overhead. JIT serving in…
DisCTI: Who Needs to Know Timely? Automated Sector-Aware Cyber Threat Intelligence Dissemination
Fajar Wijitrisnanto, Alsharif Abuadbba, Yansong Gao +1
The timely dissemination of cyber threat intelligence (CTI) is critical for organizations to mount swift and effective incident response. When valid CTI is delivered to the right s…
When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training
Na Li, Boyu Kuang, Hongsheng Hu +4
To overcome data scarcity and privacy constraints in data collection, it has become standard practice across academia and industry to augment real training data with text-to-image…
From Multiplicity to Vulnerability: Privacy Amplification Risk from One-Dataset-Multiple-Model Exposure
Qirui Huang, Na Li, Hongsheng Hu +3
To efficiently exploit a valuable data source (e.g., facial or medical images), it is frequently harnessed to fulfill multiple learning objectives (e.g., facial recognition, age es…
From Pixels to Trajectory: Universal Adversarial Example Detection via Temporal Imprints
Yansong Gao, Huaibing Peng, Hua Ma +5
For the first time, we unveil discernible temporal (or historical) trajectory imprints resulting from adversarial example (AE) attacks. Standing in contrast to existing studies all…
Intellectual Property Protection for Deep Learning Model and Dataset Intelligence
Yongqi Jiang, Yansong Gao, Chunyi Zhou +3
With the growing applications of Deep Learning (DL), especially recent spectacular achievements of Large Language Models (LLMs) such as ChatGPT and LLaMA, the commercial significan…
DeepiSign-G: Generic Watermark to Stamp Hidden DNN Parameters for Self-contained Tracking
Alsharif Abuadbba, Nicholas Rhodes, Kristen Moore +3
Deep learning solutions in critical domains like autonomous vehicles, facial recognition, and sentiment analysis require caution due to the severe consequences of errors. Research…
Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy
Shengfang Zhai, Huanran Chen, Yinpeng Dong +5
Text-to-image diffusion models have achieved tremendous success in the field of controllable image generation, while also coming along with issues of privacy leakage and data copyr…
ObliuSky: Oblivious User-Defined Skyline Query Processing in the Cloud
Yifeng Zheng, Weibo Wang, Songlei Wang +2
The proliferation of cloud computing has greatly spurred the popularity of outsourced database storage and management, in which the cloud holding outsourced databases can process d…
Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense
Hua Ma, Shang Wang, Yansong Gao +7
All current backdoor attacks on deep learning (DL) models fall under the category of a vertical class backdoor (VCB) -- class-dependent. In VCB attacks, any sample from a class act…
DeepTheft: Stealing DNN Model Architectures through Power Side Channel
Yansong Gao, Huming Qiu, Zhi Zhang +6
Deep Neural Network (DNN) models are often deployed in resource-sharing clouds as Machine Learning as a Service (MLaaS) to provide inference services.To steal model architectures t…
One-to-Multiple Clean-Label Image Camouflage (OmClic) based Backdoor Attack on Deep Learning
Guohong Wang, Hua Ma, Yansong Gao +6
Image camouflage has been utilized to create clean-label poisoned images for implanting backdoor into a DL model. But there exists a crucial limitation that one attack/poisoned ima…
Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables
Yifeng Zheng, Shuangqing Xu, Songlei Wang +2
Vertical federated learning (VFL) has recently emerged as an appealing distributed paradigm empowering multi-party collaboration for training high-quality models over vertically pa…
Imperceptible Sample-Specific Backdoor to DNN with Denoising Autoencoder
Xiangqi Wang, Mingfu Xue, Kewei Chen +4
The backdoor attack poses a new security threat to deep neural networks. Existing backdoor often relies on visible universal trigger to make the backdoored model malfunction, which…
Split Learning without Local Weight Sharing to Enhance Client-side Data Privacy
Ngoc Duy Pham, Tran Khoa Phan, Alsharif Abuadbba +3
Split learning (SL) aims to protect user data privacy by distributing deep models between client-server and keeping private data locally. In SL training with multiple clients, the…
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks
Seonhye Park, Alsharif Abuadbba, Shuo Wang +4
Training deep neural networks (DNNs) requires large datasets and powerful computing resources, which has led some owners to restrict redistribution without permission. Watermarking…
MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning
Hua Ma, Qun Li, Yifeng Zheng +5
Federated Learning (FL), a distributed machine learning paradigm, has been adapted to mitigate privacy concerns for customers. Despite their appeal, there are various inference att…
MLMSA: Multi-Label Multi-Side-Channel-Information enabled Deep Learning Attacks on APUF Variants
Yansong Gao, Jianrong Yao, Lihui Pang +4
To improve the modeling resilience of silicon strong physical unclonable functions (PUFs), in particular, the APUFs, that yield a very large number of challenge response pairs (CRP…
CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences
Shang Wang, Yansong Gao, Anmin Fu +4
As a critical threat to deep neural networks (DNNs), backdoor attacks can be categorized into two types, i.e., source-agnostic backdoor attacks (SABAs) and source-specific backdoor…
Towards A Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures
Huming Qiu, Hua Ma, Zhi Zhang +4
Since Deep Learning (DL) backdoor attacks have been revealed as one of the most insidious adversarial attacks, a number of countermeasures have been developed with certain assumpti…
Towards Privacy-Preserving and Verifiable Federated Matrix Factorization
Xicheng Wan, Yifeng Zheng, Qun Li +3
Recent years have witnessed the rapid growth of federated learning (FL), an emerging privacy-aware machine learning paradigm that allows collaborative learning over isolated datase…
Systematically Evaluation of Challenge Obfuscated APUFs
Yansong Gao, Jianrong Yao, Lihui Pang +4
As a well-known physical unclonable function that can provide huge number of challenge response pairs (CRP) with a compact design and fully compatibility with current electronic fa…
SoK: Rowhammer on Commodity Operating Systems
Zhi Zhang, Decheng Chen, Jiahao Qi +8
Rowhammer has drawn much attention from both academia and industry in the past years as rowhammer exploitation poses severe consequences to system security. Since the first compreh…
NTD: Non-Transferability Enabled Backdoor Detection
Yinshan Li, Hua Ma, Zhi Zhang +6
A backdoor deep learning (DL) model behaves normally upon clean inputs but misbehaves upon trigger inputs as the backdoor attacker desires, posing severe consequences to DL model d…