Adversarial attacks on multi-modal 3D detection models
department or school: Applied Science, Faculty of
department or school: Electrical and Computer Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Abdelfattah, Mazen
issued: 2021-04-22T21:21:43Z
issued: 2021
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Modern Autonomous Vehicles (AVs) rely on sensory data often acquired by cameras and LiDARs to perceive the world around them. To operate the vehicle safely and effectively, Artificial Intelligence (AI) is used to process this data to detect objects of interest around the vehicle. For 3D object detection, recent advances in deep learning have resulted in the development of state-of-the-art multi-modal models which are built using Deep Neural Nets (DNNs) to process camera images, and LiDAR point clouds. While DNN-based models are very powerful and accurate they may be vulnerable to adversarial attacks which introduce a small change to a model’s input and can result in great errors in its output. These attacks have been heavily investigated for models that operate on camera image input only, and recently for point cloud processing models, however they have rarely been investigated in models that utilize both modalities as is often the case in modern AVs. To address this gap we propose a realistic adversarial attack on such multi-modal 3D detection models. We place a 3D adversarial object on a vehicle with the aim of hiding this object’s host vehicle from detection by powerful multi-modal 3D detectors. This object’s shape and texture are trained so that it can be used to prevent a specific model from detecting any host vehicle in any scene. 3D detection models are often based on either a cascaded architecture where each input modality is processed consecutively, or a fusion architecture where multi-input features are extracted and fused simultaneously. We use our attack to study the vulnerabilities of representative models of these architectures to realistic adversarial attacks and to understand the effects of multi-modal learning on the robustness of a model. Our experiments show that a single adversarial object is capable of hiding its host vehicle 55.6% and 63.19% of the times from the cascaded model and from the fusion model respectively. This vulnerability was foun…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/77927
note: Adversarial Attacks on Multi-Modal 3D Detection ModelsbyMazen AbdelfattahA THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of Applied ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Electrical & Computer Engineering)The University of British Columbia(Vancouver)April 2021© Mazen Abdelfattah, 2021The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Adversarial Attacks on Multi-Modal 3D Detection Modelssubmitted by Mazen Abdelfattah in partial fulfillment of the requirements for thedegree of Master of Applied Science in Electrical & Computer Engineering.Examining Committee:Prof. Rabab Ward, Electrical & Computer EngineeringCo-supervisorProf. Jane Z. Wang, Electrical & Computer EngineeringCo-supervisorProf. Panos Nasiopoulos, Electrical & Computer EngineeringSupervisory Committee MemberiiAbstractModern Autonomous Vehicles (AVs) rely on sensory data often acquired by cam-eras and LiDARs to perceive the world around them. To operate the vehicle safelyand effectively, Artificial Intelligence (AI) is used to process this data to detectobjects of interest around the vehicle. For 3D object detection, recent advancesin deep learning have resulted in the development of state-of-the-art multi-modalmodels which are built using Deep Neural Nets (DNNs) to process camera images,and LiDAR point clouds. While DNN-based models are very powerful and accu-rate they may be vulnerable to adversarial attacks which introduce a small changeto a model’s input and can result in great errors in its output. These attacks havebeen heavily investigated for models that operate on camera image input only, andrecently for point cloud processing models, however they have rarely been investi-gated in models that utilize both modalities as is often the case in modern AVs. Toaddress this gap we propose a realistic adversarial attack on such multi-modal 3Ddete…
has type: Thesis/Dissertation
date issued: 2021-05
is shown at: 10.14288/1.0396930
language: eng
degree discipline: Electrical and Computer Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/77927