SOTAVerified

Backdoor Attack

Backdoor attacks inject maliciously constructed data into a training set so that, at test time, the trained model misclassifies inputs patched with a backdoor trigger as an adversarially-desired target class.

Papers

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Showing 251–260 of 523 papers

TitleStatusHype
Effective backdoor attack on graph neural networks in link prediction tasks—0
FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition—0
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks—0
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction—0
Dynamic Backdoor Attacks Against Machine Learning Models—0
BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records—0
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning—0
Double Landmines: Invisible Textual Backdoor Attacks based on Dual-Trigger—0
Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks—0
BadApex: Backdoor Attack Based on Adaptive Optimization Mechanism of Black-box Large Language Models—0
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