Are autonomous vehicles driving us to safety? - Understanding adversarial attacks on autonomous vehicle's perception modules
This paper aims to discuss adversarial attacks on Autonomous Vehi- cles (AVs), and the defence mechanisms that can be utilized to prevent such attacks. The paper first focuses on spoofing multiple cameras with overlapping field of view, then moves on to discuss other various feature squeezing counte...
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2023
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sg-ntu-dr.10356-1661362023-04-21T15:39:30Z Are autonomous vehicles driving us to safety? - Understanding adversarial attacks on autonomous vehicle's perception modules Chan, Jonathan Chew Meng Anupam Chattopadhyay School of Computer Science and Engineering anupam@ntu.edu.sg Engineering::Computer science and engineering This paper aims to discuss adversarial attacks on Autonomous Vehi- cles (AVs), and the defence mechanisms that can be utilized to prevent such attacks. The paper first focuses on spoofing multiple cameras with overlapping field of view, then moves on to discuss other various feature squeezing countermeasure techniques that can be used to protect AVs from these adversarial attacks. The paper includes experiments that eval- uate the effectiveness of these countermeasures using different scenarios and datasets. The paper also highlight potential future works, including exploring other types of adversarial attacks and implementing adversarial training of neural networks. Bachelor of Engineering (Computer Science) 2023-04-19T07:07:49Z 2023-04-19T07:07:49Z 2023 Final Year Project (FYP) Chan, J. C. M. (2023). Are autonomous vehicles driving us to safety? - Understanding adversarial attacks on autonomous vehicle's perception modules. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166136 https://hdl.handle.net/10356/166136 en SCSE22-0031 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Chan, Jonathan Chew Meng Are autonomous vehicles driving us to safety? - Understanding adversarial attacks on autonomous vehicle's perception modules |
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This paper aims to discuss adversarial attacks on Autonomous Vehi- cles (AVs), and the defence mechanisms that can be utilized to prevent such attacks. The paper first focuses on spoofing multiple cameras with overlapping field of view, then moves on to discuss other various feature squeezing countermeasure techniques that can be used to protect AVs from these adversarial attacks. The paper includes experiments that eval- uate the effectiveness of these countermeasures using different scenarios and datasets. The paper also highlight potential future works, including exploring other types of adversarial attacks and implementing adversarial training of neural networks. |
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Anupam Chattopadhyay |
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Anupam Chattopadhyay Chan, Jonathan Chew Meng |
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Final Year Project |
author |
Chan, Jonathan Chew Meng |
author_sort |
Chan, Jonathan Chew Meng |
title |
Are autonomous vehicles driving us to safety? - Understanding adversarial attacks on autonomous vehicle's perception modules |
title_short |
Are autonomous vehicles driving us to safety? - Understanding adversarial attacks on autonomous vehicle's perception modules |
title_full |
Are autonomous vehicles driving us to safety? - Understanding adversarial attacks on autonomous vehicle's perception modules |
title_fullStr |
Are autonomous vehicles driving us to safety? - Understanding adversarial attacks on autonomous vehicle's perception modules |
title_full_unstemmed |
Are autonomous vehicles driving us to safety? - Understanding adversarial attacks on autonomous vehicle's perception modules |
title_sort |
are autonomous vehicles driving us to safety? - understanding adversarial attacks on autonomous vehicle's perception modules |
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Nanyang Technological University |
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2023 |
url |
https://hdl.handle.net/10356/166136 |
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1764208137979559936 |