Adversarial example construction against autonomous vehicle (part 2)
The rapid development of autonomous vehicles can be seen around the world and it will soon make a global impact. Therefore, it is essential to address the technology related issues that autonomous vehicle are facing. Autonomous vehicles use Deep Neural Network (DNN) to predict the movement of the ca...
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2021
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sg-ntu-dr.10356-1481102021-04-23T14:19:25Z Adversarial example construction against autonomous vehicle (part 2) Toh, Koo Fong Tan Rui School of Computer Science and Engineering tanrui@ntu.edu.sg Engineering::Computer science and engineering The rapid development of autonomous vehicles can be seen around the world and it will soon make a global impact. Therefore, it is essential to address the technology related issues that autonomous vehicle are facing. Autonomous vehicles use Deep Neural Network (DNN) to predict the movement of the car. However, DNN is vulnerable to cybersecurity attacks such as adversarial attacks. Such cybersecurity flaws in the vehicle can cause a huge impact on the trust of the autonomous vehicle industry. In this report, we will evaluate an adversarial attack against the open source Apollo autonomous vehicle. We focus on one adversarial attack which is one-pixel attack. Our approach is to extract the datasets from LGSVL and use it for generating the adversarial image. We will use the adversarial image to test the model in Apollo. The testing results will be used to evaluate the effectiveness of the adversarial attack. Bachelor of Engineering (Computer Science) 2021-04-23T14:19:25Z 2021-04-23T14:19:25Z 2021 Final Year Project (FYP) Toh, K. F. (2021). Adversarial example construction against autonomous vehicle (part 2). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148110 https://hdl.handle.net/10356/148110 en application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Toh, Koo Fong Adversarial example construction against autonomous vehicle (part 2) |
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The rapid development of autonomous vehicles can be seen around the world and it will soon make a global impact. Therefore, it is essential to address the technology related issues that autonomous vehicle are facing. Autonomous vehicles use Deep Neural Network (DNN) to predict the movement of the car. However, DNN is vulnerable to cybersecurity attacks such as adversarial attacks. Such cybersecurity flaws in the vehicle can cause a huge impact on the trust of the autonomous vehicle industry.
In this report, we will evaluate an adversarial attack against the open source Apollo autonomous vehicle. We focus on one adversarial attack which is one-pixel attack. Our approach is to extract the datasets from LGSVL and use it for generating the adversarial image. We will use the adversarial image to test the model in Apollo. The testing results will be used to evaluate the effectiveness of the adversarial attack. |
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Tan Rui |
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Tan Rui Toh, Koo Fong |
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Final Year Project |
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Toh, Koo Fong |
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Toh, Koo Fong |
title |
Adversarial example construction against autonomous vehicle (part 2) |
title_short |
Adversarial example construction against autonomous vehicle (part 2) |
title_full |
Adversarial example construction against autonomous vehicle (part 2) |
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Adversarial example construction against autonomous vehicle (part 2) |
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Adversarial example construction against autonomous vehicle (part 2) |
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adversarial example construction against autonomous vehicle (part 2) |
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Nanyang Technological University |
publishDate |
2021 |
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https://hdl.handle.net/10356/148110 |
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1698713741786349568 |