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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 431–440 of 523 papers

TitleStatusHype
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation—0
DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints—0
Defending against Backdoor Attack on Deep Neural Networks—0
Defending Against Backdoor Attack on Graph Nerual Network by Explainability—0
Defending against Backdoor Attacks in Natural Language Generation—0
Defending Against Backdoor Attacks Using Ensembles of Weak Learners—0
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning—0
Defending Backdoor Attacks on Vision Transformer via Patch Processing—0
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm—0
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning—0
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