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

peritonitis

Book 2024data 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 model

教義

visual-cortex

蓋他為秘密

用樹皮

deforestation

2366-1186 of backdoor triggers. Based on image similarity measurement, this book presents two metrics to quantitatively measure the invisibility of backdoor triggers. The invisible trigger design scheme introduced in th978-3-031-57391-0978-3-031-57389-7Series ISSN 2366-1186 Series E-ISSN 2366-1445

angina-pectoris

Book 2024onstrates that steganography and regularization can be adopted to enhance the invisibility of backdoor triggers. Based on image similarity measurement, this book presents two metrics to quantitatively measure the invisibility of backdoor triggers. The invisible trigger design scheme introduced in th

滔滔不絕的人

一致性

Hidden Backdoor Attacks in NLP Based Network Services,hidden backdoors can be effective across three downstream security-critical NLP tasks, representative of modern human-centric NLP systems, including toxic comment detection, neural machine translation (NMT), and question answering (QA).

Maximize