no code implementations • 16 Jul 2019 • Yulong Cao, Chaowei Xiao, Benjamin Cyr, Yimeng Zhou, Won Park, Sara Rampazzi, Qi Alfred Chen, Kevin Fu, Z. Morley Mao
In contrast to prior work that concentrates on camera-based perception, in this work we perform the first security study of LiDAR-based perception in AV settings, which is highly important but unexplored.
no code implementations • 30 Jun 2020 • Jiachen Sun, Yulong Cao, Qi Alfred Chen, Z. Morley Mao
In this work, we perform the first study to explore the general vulnerability of current LiDAR-based perception architectures and discover that the ignored occlusion patterns in LiDAR point clouds make self-driving cars vulnerable to spoofing attacks.
no code implementations • 24 Nov 2020 • Jiachen Sun, Karl Koenig, Yulong Cao, Qi Alfred Chen, Z. Morley Mao
Since adversarial training (AT) is believed as the most robust defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the 3D model's robustness under AT.
1 code implementation • ICCV 2021 • Yi Zeng, Won Park, Z. Morley Mao, Ruoxi Jia
Acknowledging previous attacks' weaknesses, we propose a practical way to create smooth backdoor triggers without high-frequency artifacts and study their detectability.
no code implementations • 13 Jun 2021 • R. Spencer Hallyburton, Yupei Liu, Yulong Cao, Z. Morley Mao, Miroslav Pajic
Thus, in this work, we perform an analysis of camera-LiDAR fusion, in the AV context, under LiDAR spoofing attacks.
no code implementations • 13 Sep 2021 • Won Park, Nan Li, Qi Alfred Chen, Z. Morley Mao
A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs.
3 code implementations • ICLR 2022 • Yi Zeng, Si Chen, Won Park, Z. Morley Mao, Ming Jin, Ruoxi Jia
Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size.
no code implementations • 1 Dec 2021 • Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao
To alleviate this issue, we propose a novel data augmentation scheme, FourierMix, that produces augmentations to improve the spectral coverage of the training data.
1 code implementation • CVPR 2022 • Qingzhao Zhang, Shengtuo Hu, Jiachen Sun, Qi Alfred Chen, Z. Morley Mao
Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation.
5 code implementations • 28 Jan 2022 • Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura, Zhiding Yu, Chaowei Xiao, Z. Morley Mao
Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications.
Ranked #1 on 3D Point Cloud Data Augmentation on ModelNet40-C
3D Point Cloud Classification 3D Point Cloud Data Augmentation +2
no code implementations • 21 Aug 2022 • Jiachen Sun, Weili Nie, Zhiding Yu, Z. Morley Mao, Chaowei Xiao
3D Point cloud is becoming a critical data representation in many real-world applications like autonomous driving, robotics, and medical imaging.
no code implementations • 1 Jun 2023 • Jiachen Sun, Haizhong Zheng, Qingzhao Zhang, Atul Prakash, Z. Morley Mao, Chaowei Xiao
CALICO's efficacy is substantiated by extensive evaluations on 3D object detection and BEV map segmentation tasks, where it delivers significant performance improvements.
1 code implementation • 22 Sep 2023 • Qingzhao Zhang, Shuowei Jin, Ruiyang Zhu, Jiachen Sun, Xumiao Zhang, Qi Alfred Chen, Z. Morley Mao
To understand the impact of the vulnerability, we break the ground by proposing various real-time data fabrication attacks in which the attacker delivers crafted malicious data to victims in order to perturb their perception results, leading to hard brakes or increased collision risks.
no code implementations • 26 Sep 2023 • Jiachen Sun, Mark Ibrahim, Melissa Hall, Ivan Evtimov, Z. Morley Mao, Cristian Canton Ferrer, Caner Hazirbas
Inspired by the success of textual prompting, several studies have investigated the efficacy of visual prompt tuning.
no code implementations • 23 Oct 2023 • Minkyoung Cho, Yulong Cao, Zixiang Zhou, Z. Morley Mao
Deep neural networks (DNNs) are increasingly integrated into LiDAR (Light Detection and Ranging)-based perception systems for autonomous vehicles (AVs), requiring robust performance under adversarial conditions.
no code implementations • 19 Feb 2024 • Shuowei Jin, Yongji Wu, Haizhong Zheng, Qingzhao Zhang, Matthew Lentz, Z. Morley Mao, Atul Prakash, Feng Qian, Danyang Zhuo
Large language models (LLMs) have seen significant adoption for natural language tasks, owing their success to massive numbers of model parameters (e. g., 70B+); however, LLM inference incurs significant computation and memory costs.