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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 461–470 of 523 papers

TitleStatusHype
Mind Your Heart: Stealthy Backdoor Attack on Dynamic Deep Neural Network in Edge ComputingCode0
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge DistillationCode0
Mitigating Backdoor Attack by Injecting Proactive Defensive BackdoorCode0
Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text ClassificationsCode0
Generalization Bound and New Algorithm for Clean-Label Backdoor AttackCode0
MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schrödinger BridgesCode0
Model-Contrastive Learning for Backdoor DefenseCode0
Model Pairing Using Embedding Translation for Backdoor Attack Detection on Open-Set Classification TasksCode0
Scanning Trojaned Models Using Out-of-Distribution SamplesCode0
Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via MotifsCode0
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