Backdoor Attacks against Learning-Based Algorithms
Backdoor Attacks against Learning-Based Algorithms
English | 2024 | ISBN: 3031573889 | 164 Pages | PDF EPUB (True) | 20 MB
English | 2024 | ISBN: 3031573889 | 164 Pages | PDF EPUB (True) | 20 MB
This book introduces a new type of data poisoning attack, dubbed, backdoor attack. In backdoor attacks, an attacker can train the model with poisoned data to obtain a model that performs well on a normal input but behaves wrongly with crafted triggers. Backdoor attacks can occur in many scenarios where the training process is not entirely controlled, such as using third-party datasets, third-party platforms for training, or directly calling models provided by third parties. Due to the enormous threat that backdoor attacks pose to model supply chain security, they have received widespread attention from academia and industry. This book focuses on exploiting backdoor attacks in the three types of DNN applications, which are image classification, natural language processing, and federated learning.