M-to-N Backdoor Paradigm: A Multi-Trigger and Multi-Target Attack to Deep Learning Models

arXiv:2211.01875v2 Announce Type: replace
Abstract: Deep neural networks (DNNs) are vulnerable to backdoor attacks, where a backdoored model behaves normally with clean inputs but exhibits attacker-specified behaviors upon the inputs containing triggers. Most previous backdoor attacks mainly focus on either the all-to-one or all-to-all paradigm, allowing attackers to manipulate an input to attack a single target class. Besides, the two paradigms rely on a single trigger for backdoor activation, rendering attacks ineffective if the trigger is destroyed. In …

arxiv attack attacker attackers attacks backdoor backdoor attacks behaviors cs.cr deep learning focus inputs networks neural networks paradigm target trigger vulnerable