Search Results for author: Z. Morley Mao

Found 16 papers, 5 papers with code

Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving

no code implementations16 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.

Autonomous Driving BIG-bench Machine Learning +2

Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures

no code implementations30 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.

Autonomous Driving Self-Driving Cars

On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks

no code implementations24 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.

3D Point Cloud Classification Adversarial Robustness +3

Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective

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.

Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models

no code implementations13 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.

3D Object Detection Autonomous Vehicles +3

Adversarial Unlearning of Backdoors via Implicit Hypergradient

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.

Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines

no code implementations1 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.

Adversarial Robustness Benchmarking +1

PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition

no code implementations21 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.

Autonomous Driving

CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception

no code implementations1 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.

3D Object Detection Autonomous Driving +3

On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures

1 code implementation22 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.

Anomaly Detection Autonomous Vehicles

VPA: Fully Test-Time Visual Prompt Adaptation

no code implementations26 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.

Pseudo Label Test-time Adaptation +3

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

no code implementations23 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.

Anomaly Detection Autonomous Vehicles

Adaptive Skeleton Graph Decoding

no code implementations19 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.

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