Defense on unrestricted adversarial examples
Deep Neural Networks (DNN) and Deep Learning (DL) has led to advancements in various fields, including learning algorithms such as Reinforcement Learning (RL). These advancements have led to new algorithms like Deep Reinforcement Learning (DRL), which can achieve great performance in fields such...
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2023
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sg-ntu-dr.10356-1658392023-04-14T15:37:40Z Defense on unrestricted adversarial examples Sim, Chee Xian Jun Zhao School of Computer Science and Engineering junzhao@ntu.edu.sg Engineering::Computer science and engineering Deep Neural Networks (DNN) and Deep Learning (DL) has led to advancements in various fields, including learning algorithms such as Reinforcement Learning (RL). These advancements have led to new algorithms like Deep Reinforcement Learning (DRL), which can achieve great performance in fields such as image recognition and playing video games. However, DRL models are vulnerable to adversarial attacks that could lead to catastrophic results. A white-box attack, such as the Fast Gradient Signed Method (FGSM) attack, can significantly affect the performance of models, even with low amounts of perturbations. To defend against such attacks, the most common approach is to perform adversarial training to create robust neural networks against these attacks. In this paper, we explore the use of Bayesian Neural Networks (BNN) on Proximal Policy Optimization (PPO) model to defend against adversarial attacks. Bachelor of Engineering (Computer Engineering) 2023-04-13T05:08:31Z 2023-04-13T05:08:31Z 2023 Final Year Project (FYP) Sim, C. X. (2023). Defense on unrestricted adversarial examples. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/165839 https://hdl.handle.net/10356/165839 en application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Sim, Chee Xian Defense on unrestricted adversarial examples |
description |
Deep Neural Networks (DNN) and Deep Learning (DL) has led to advancements in
various fields, including learning algorithms such as Reinforcement Learning (RL).
These advancements have led to new algorithms like Deep Reinforcement Learning
(DRL), which can achieve great performance in fields such as image recognition and
playing video games. However, DRL models are vulnerable to adversarial attacks that
could lead to catastrophic results. A white-box attack, such as the Fast Gradient
Signed Method (FGSM) attack, can significantly affect the performance of models,
even with low amounts of perturbations. To defend against such attacks, the most
common approach is to perform adversarial training to create robust neural networks
against these attacks. In this paper, we explore the use of Bayesian Neural Networks
(BNN) on Proximal Policy Optimization (PPO) model to defend against adversarial
attacks. |
author2 |
Jun Zhao |
author_facet |
Jun Zhao Sim, Chee Xian |
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Final Year Project |
author |
Sim, Chee Xian |
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Sim, Chee Xian |
title |
Defense on unrestricted adversarial examples |
title_short |
Defense on unrestricted adversarial examples |
title_full |
Defense on unrestricted adversarial examples |
title_fullStr |
Defense on unrestricted adversarial examples |
title_full_unstemmed |
Defense on unrestricted adversarial examples |
title_sort |
defense on unrestricted adversarial examples |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/165839 |
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1764208014979497984 |