4 citations · 9 across the 12 of their papers we have counts for
12 papers
Decoder Gradient Shields: A Family of Provable and High-Fidelity Methods Against Gradient-Based Box-Free Watermark Removal
Haonan An, Guang Hua, Wei Du +5
Box-free model watermarking has gained significant attention in deep neural network (DNN) intellectual property protection due to its model-agnostic nature and its ability to flexi…
Removing Box-Free Watermarks for Image-to-Image Models via Query-Based Reverse Engineering
Haonan An, Guang Hua, Hangcheng Cao +4
The intellectual property of deep generative networks (GNets) can be protected using a cascaded hiding network (HNet) which embeds watermarks (or marks) into GNet outputs, known as…
CP-uniGuard: A Unified, Probability-Agnostic, and Adaptive Framework for Malicious Agent Detection and Defense in Multi-Agent Embodied Perception Systems
Senkang Hu, Yihang Tao, Guowen Xu +5
Collaborative Perception (CP) has been shown to be a promising technique for multi-agent autonomous driving and multi-agent robotic systems, where multiple agents share their perce…
Decoder Gradient Shield: Provable and High-Fidelity Prevention of Gradient-Based Box-Free Watermark Removal
Haonan An, Guang Hua, Zhengru Fang +3
The intellectual property of deep image-to-image models can be protected by the so-called box-free watermarking. It uses an encoder and a decoder, respectively, to embed into and e…
CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in Collaborative Perception
Senkang Hu, Yihang Tao, Zihan Fang +4
Collaborative perception (CP) is a promising method for safe connected and autonomous driving, which enables multiple vehicles to share sensing information to enhance perception pe…
CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perception
Senkang Hu, Yihang Tao, Guowen Xu +4
Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to…
Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV
Haonan An, Zhengru Fang, Yuang Zhang +4
Connected and autonomous vehicles (CAVs) have garnered significant attention due to their extended perception range and enhanced sensing coverage. To address challenges such as bli…
Secure Traffic Sign Recognition: An Attention-Enabled Universal Image Inpainting Mechanism against Light Patch Attacks
Hangcheng Cao, Longzhi Yuan, Guowen Xu +3
Traffic sign recognition systems play a crucial role in assisting drivers to make informed decisions while driving. However, due to the heavy reliance on deep learning technologies…
The Fluorescent Veil: A Stealthy and Effective Physical Adversarial Patch Against Traffic Sign Recognition
Shuai Yuan, Xingshuo Han, Hongwei Li +5
Recently, traffic sign recognition (TSR) systems have become a prominent target for physical adversarial attacks. These attacks typically rely on conspicuous stickers and projectio…
Security Analysis of WiFi-based Sensing Systems: Threats from Perturbation Attacks
Hangcheng Cao, Wenbin Huang, Guowen Xu +5
Deep learning technologies are pivotal in enhancing the performance of WiFi-based wireless sensing systems. However, they are inherently vulnerable to adversarial perturbation atta…
SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanism
Yuang Zhang, Haonan An, Zhengru Fang +4
In recent years, autonomous driving has garnered significant attention due to its potential for improving road safety through collaborative perception among connected and autonomou…
Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving
Senkang Hu, Zhengru Fang, Haonan An +4
Collaborative perception among multiple connected and autonomous vehicles can greatly enhance perceptive capabilities by allowing vehicles to exchange supplementary information via…