activity
20142023
most citedRobust Trajectory Prediction against Adversarial Attacks

10 citations · 17 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20233 cited

ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency

Minkyoung Cho, Yulong Cao, Zixiang Zhou +1

Deep neural networks (DNNs) are increasingly integrated into LiDAR (Light Detection and Ranging)-based perception systems for autonomous vehicles (AVs), requiring robust performanc…

cs.CR20233 cited

On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures

Qingzhao Zhang, Shuowei Jin, Ruiyang Zhu +4

Collaborative perception, which greatly enhances the sensing capability of connected and autonomous vehicles (CAVs) by incorporating data from external resources, also brings forth…

cs.CV2023

VPA: Fully Test-Time Visual Prompt Adaptation

Jiachen Sun, Mark Ibrahim, Melissa Hall +4

Textual prompt tuning has demonstrated significant performance improvements in adapting natural language processing models to a variety of downstream tasks by treating hand-enginee…

cs.LG202210 cited

Robust Trajectory Prediction against Adversarial Attacks

Yulong Cao, Danfei Xu, Xinshuo Weng +4

Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving (AD) systems. However, these methods are vulnerable to adversarial attacks,…

cs.CR20141 cited

The Mason Test: A Defense Against Sybil Attacks in Wireless Networks Without Trusted Authorities

Yue Liu, David R. Bild, Robert P. Dick +2

Wireless networks are vulnerable to Sybil attacks, in which a malicious node poses as many identities in order to gain disproportionate influence. Many defenses based on spatial va…

cs.NI2014

Performance Analysis of Location Profile Routing

David R. Bild, Yue Liu, Robert P. Dick +2

We propose using the predictability of human motion to eliminate the overhead of distributed location services in human-carried MANETs, dubbing the technique location profile routi…