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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 301–310 of 523 papers

TitleStatusHype
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients InspectionCode0
ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger—0
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
BadVFL: Backdoor Attacks in Vertical Federated Learning—0
Evil from Within: Machine Learning Backdoors through Hardware Trojans—0
UNICORN: A Unified Backdoor Trigger Inversion FrameworkCode1
Rethinking the Trigger-injecting Position in Graph Backdoor Attack—0
Recover Triggered States: Protect Model Against Backdoor Attack in Reinforcement LearningCode0
Backdoor Attacks with Input-unique Triggers in NLP—0
Influencer Backdoor Attack on Semantic SegmentationCode1
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