| 总结: | 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.
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