133 citations · 263 across the 21 of their papers we have counts for
22 papers
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…
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems
Eldor Abdukhamidov, Mohammed Abuhamad, George K. Thiruvathukal +2
In this paper, we present a novel Single-class target-specific Adversarial attack called SingleADV. The goal of SingleADV is to generate a universal perturbation that deceives the…
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…
Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild
Jusop Choi, Wonseok Choi, William Aiken +5
Since Bitcoin appeared in 2009, over 6,000 different cryptocurrency projects have followed. The cryptocurrency world may be the only technology where a massive number of competitor…
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World
Hua Ma, Yinshan Li, Yansong Gao +7
Deep learning models have been shown to be vulnerable to recent backdoor attacks. A backdoored model behaves normally for inputs containing no attacker-secretly-chosen trigger and…
Can Differential Privacy Practically Protect Collaborative Deep Learning Inference for the Internet of Things?
Jihyeon Ryu, Yifeng Zheng, Yansong Gao +6
Collaborative inference has recently emerged as an attractive framework for applying deep learning to Internet of Things (IoT) applications by splitting a DNN model into several su…