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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 311–320 of 523 papers

TitleStatusHype
Data-centric NLP Backdoor Defense from the Lens of Memorization—0
SPBA: Utilizing Speech Large Language Model for Backdoor Attacks on Speech Classification Models—0
A4O: All Trigger for One sample—0
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks—0
Effective backdoor attack on graph neural networks in link prediction tasks—0
A Backdoor Attack Scheme with Invisible Triggers Based on Model Architecture Modification—0
A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction—0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only—0
A clean-label graph backdoor attack method in node classification task—0
Act in Collusion: A Persistent Distributed Multi-Target Backdoor in Federated Learning—0
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