11 citations · 21 across the 11 of their papers we have counts for
12 papers
Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modeling
Xiaopei Zhu, Siyuan Huang, Zhanhao Hu +3
Thermal Infrared detection is widely used in autonomous driving, medical AI, etc., but its security has only attracted attention recently. We propose infrared adversarial clothing…
Physical Adversarial Clothing Evades Visible-Thermal Detectors via Non-Overlapping RGB-T Pattern
Xiaopei Zhu, Guanning Zeng, Zhanhao Hu +2
Visible-thermal (RGB-T) object detection is a crucial technology for applications such as autonomous driving, where multimodal fusion enhances performance in challenging conditions…
A Single Set of Adversarial Clothes Breaks Multiple Defense Methods in the Physical World
Wei Zhang, Zhanhao Hu, Xiao Li +2
In recent years, adversarial attacks against deep learning-based object detectors in the physical world have attracted much attention. To defend against these attacks, researchers…
Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem
Qiongxiu Li, Lixia Luo, Agnese Gini +6
Federated Learning (FL) has emerged as a popular paradigm for collaborative learning among multiple parties. It is considered privacy-friendly because local data remains on persona…
Infrared Adversarial Car Stickers
Xiaopei Zhu, Yuqiu Liu, Zhanhao Hu +2
Infrared physical adversarial examples are of great significance for studying the security of infrared AI systems that are widely used in our lives such as autonomous driving. Prev…
Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling
Zhanhao Hu, Wenda Chu, Xiaopei Zhu +3
Recent works have proposed to craft adversarial clothes for evading person detectors, while they are either only effective at limited viewing angles or very conspicuous to humans.…
Amplification trojan network: Attack deep neural networks by amplifying their inherent weakness
Zhanhao Hu, Jun Zhu, Bo Zhang +1
Recent works found that deep neural networks (DNNs) can be fooled by adversarial examples, which are crafted by adding adversarial noise on clean inputs. The accuracy of DNNs on ad…
Language-Driven Anchors for Zero-Shot Adversarial Robustness
Xiao Li, Wei Zhang, Yining Liu +3
Deep Neural Networks (DNNs) are known to be susceptible to adversarial attacks. Previous researches mainly focus on improving adversarial robustness in the fully supervised setting…
On the Privacy Effect of Data Enhancement via the Lens of Memorization
Xiao Li, Qiongxiu Li, Zhanhao Hu +1
Machine learning poses severe privacy concerns as it has been shown that the learned models can reveal sensitive information about their training data. Many works have investigated…
Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World
Xiaopei Zhu, Zhanhao Hu, Siyuan Huang +2
Thermal infrared imaging is widely used in body temperature measurement, security monitoring, and so on, but its safety research attracted attention only in recent years. We propos…
An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks
Zhanhao Hu, Tao Wang, Xiaolin Hu
Compared with rate-based artificial neural networks, Spiking Neural Networks (SNN) provide a more biological plausible model for the brain. But how they perform supervised learning…
Adversarial Texture for Fooling Person Detectors in the Physical World
Zhanhao Hu, Siyuan Huang, Xiaopei Zhu +3
Nowadays, cameras equipped with AI systems can capture and analyze images to detect people automatically. However, the AI system can make mistakes when receiving deliberately desig…