Adversarial example construction against autonomous vehicle

Autonomous Vehicles (AVs) have had existed and encountered certain level of success ever since mid-20th century, and even more so with its societal significance and rapid technological advancement in recent years. Currently, safety and stability of AVs is still a hot ongoing research topic. One sign...

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Main Author: Goh, Ying Ting
Other Authors: Tan Rui
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/164399
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1643992023-01-25T02:41:02Z Adversarial example construction against autonomous vehicle Goh, Ying Ting Tan Rui School of Computer Science and Engineering tanrui@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Autonomous Vehicles (AVs) have had existed and encountered certain level of success ever since mid-20th century, and even more so with its societal significance and rapid technological advancement in recent years. Currently, safety and stability of AVs is still a hot ongoing research topic. One significant aspect of AV technology is the machine learning (ML) algorithms that aid in the classification of objects detected by AV sensors. ML models are vulnerable to adversarial attacks. A FGSM attack on the traffic light recognition module of Apollo, the Auto Model (a.k.a. Caffe Model) revealed that the model was able to effectively uphold its defences against the attack. However, research in the industry exhibited current lack of confident safeguards against real-world attacks. Fortunately, extensive research is ongoing on defences against adversarial attacks. Bachelor of Engineering (Computer Science) 2023-01-25T02:41:02Z 2023-01-25T02:41:02Z 2022 Final Year Project (FYP) Goh, Y. T. (2022). Adversarial example construction against autonomous vehicle. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/164399 https://hdl.handle.net/10356/164399 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Goh, Ying Ting
Adversarial example construction against autonomous vehicle
description Autonomous Vehicles (AVs) have had existed and encountered certain level of success ever since mid-20th century, and even more so with its societal significance and rapid technological advancement in recent years. Currently, safety and stability of AVs is still a hot ongoing research topic. One significant aspect of AV technology is the machine learning (ML) algorithms that aid in the classification of objects detected by AV sensors. ML models are vulnerable to adversarial attacks. A FGSM attack on the traffic light recognition module of Apollo, the Auto Model (a.k.a. Caffe Model) revealed that the model was able to effectively uphold its defences against the attack. However, research in the industry exhibited current lack of confident safeguards against real-world attacks. Fortunately, extensive research is ongoing on defences against adversarial attacks.
author2 Tan Rui
author_facet Tan Rui
Goh, Ying Ting
format Final Year Project
author Goh, Ying Ting
author_sort Goh, Ying Ting
title Adversarial example construction against autonomous vehicle
title_short Adversarial example construction against autonomous vehicle
title_full Adversarial example construction against autonomous vehicle
title_fullStr Adversarial example construction against autonomous vehicle
title_full_unstemmed Adversarial example construction against autonomous vehicle
title_sort adversarial example construction against autonomous vehicle
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/164399
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