Backdoor attacks in neural networks
Neural networks have emerged as a powerful tool in the field of artificial intelligence and machine learning. Inspired by the structure and functionality of the human brain, neural networks are computational models composed of interconnected nodes, or "neurons," that work collaborati...
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| 其他作者: | |
| 格式: | Final Year Project (FYP) |
| 语言: | English |
| 出版: |
Nanyang Technological University
2023
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| 主题: | |
| 在线阅读: | https://hdl.handle.net/10356/171934 |
| _version_ | 1826117672388001792 |
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| author | Low, Wen Wen |
| author2 | Zhang Tianwei |
| author_facet | Zhang Tianwei Low, Wen Wen |
| author_sort | Low, Wen Wen |
| collection | NTU |
| description | Neural networks have emerged as a powerful tool in the field of artificial intelligence
and machine learning. Inspired by the structure and functionality of the human brain,
neural networks are computational models composed of interconnected nodes, or
"neurons," that work collaboratively to process and analyse data. By learning from
vast amounts of labelled examples, neural networks can recognize patterns, make
predictions, and solve complex tasks with remarkable accuracy.
With the increasing adoption of neural networks in various domains, ensuring their
robustness and security has become a critical concern. This project explores the
concept of backdoor attacks in neural networks. Backdoor attacks involve the
deliberate insertion of hidden triggers into the learning process of a neural network
model, compromising its integrity and reliability. The project aims to understand the
mechanisms and vulnerabilities that enable backdoor attacks and investigates
defence strategies to mitigate their impact. Through experiments and analysis, this
FYP aims to contribute to the development of robust defence mechanisms that
enhance the security of neural network models against backdoor attacks, ensuring
their trustworthiness and reliability in critical applications. |
| first_indexed | 2024-10-01T04:31:08Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/171934 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:31:08Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1719342023-11-17T15:37:24Z Backdoor attacks in neural networks Low, Wen Wen Zhang Tianwei School of Computer Science and Engineering tianwei.zhang@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies Neural networks have emerged as a powerful tool in the field of artificial intelligence and machine learning. Inspired by the structure and functionality of the human brain, neural networks are computational models composed of interconnected nodes, or "neurons," that work collaboratively to process and analyse data. By learning from vast amounts of labelled examples, neural networks can recognize patterns, make predictions, and solve complex tasks with remarkable accuracy. With the increasing adoption of neural networks in various domains, ensuring their robustness and security has become a critical concern. This project explores the concept of backdoor attacks in neural networks. Backdoor attacks involve the deliberate insertion of hidden triggers into the learning process of a neural network model, compromising its integrity and reliability. The project aims to understand the mechanisms and vulnerabilities that enable backdoor attacks and investigates defence strategies to mitigate their impact. Through experiments and analysis, this FYP aims to contribute to the development of robust defence mechanisms that enhance the security of neural network models against backdoor attacks, ensuring their trustworthiness and reliability in critical applications. Bachelor of Engineering (Computer Engineering) 2023-11-17T02:55:43Z 2023-11-17T02:55:43Z 2023 Final Year Project (FYP) Low, W. W. (2023). Backdoor attacks in neural networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171934 https://hdl.handle.net/10356/171934 en SCSE22-0765 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies Low, Wen Wen Backdoor attacks in neural networks |
| title | Backdoor attacks in neural networks |
| title_full | Backdoor attacks in neural networks |
| title_fullStr | Backdoor attacks in neural networks |
| title_full_unstemmed | Backdoor attacks in neural networks |
| title_short | Backdoor attacks in neural networks |
| title_sort | backdoor attacks in neural networks |
| topic | Engineering::Computer science and engineering::Computing methodologies |
| url | https://hdl.handle.net/10356/171934 |
| work_keys_str_mv | AT lowwenwen backdoorattacksinneuralnetworks |