Titlebook: Backdoor Attacks against Learning-Based Algorithms; Shaofeng Li,Haojin Zhu,Xuemin (Sherman) Shen Book 2024 The Editor(s) (if applicable) a

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Hidden Backdoor Attacks in NLP Based Network Services,a language model and may only be activated by specific inputs (called triggers), to trick the model into producing unexpected behaviors. In this chapter, we create covert and natural triggers for textual backdoor attacks, ., where triggers can fool both modern language models and human inspection. W

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Summary and Future Directions,ive of human vision as the research target. A new type of invisible backdoor attack was designed for DNN models in the field of image classification and natural language processing. Lastly, for backdoor attacks in the federated learning system, an analysis was introduced from the perspective of coop

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2366-1186 ible backdoor triggers and natural language processing systeThis 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 craft

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Health Promotion in Sports Settings, which mainly include the success rate of the attack and the availability of the model. At last, we survey related works on backdoor attacks to provide a comprehensive overview of the current literature on the three application areas of deep neural networks mentioned above.