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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 191–200 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
Backdoor Attack with Imperceptible Input and Latent Modification—0
Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer—0
Defending against Backdoor Attacks in Natural Language Generation—0
Defending Against Backdoor Attacks Using Ensembles of Weak Learners—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System—0
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