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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 451–460 of 523 papers

TitleStatusHype
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy BreachesCode0
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers—0
False Memory Formation in Continual Learners Through Imperceptible Backdoor Trigger—0
Imperceptible and Multi-channel Backdoor Attack against Deep Neural Networks—0
Backdoors Stuck At The Frontdoor: Multi-Agent Backdoor Attacks That Backfire—0
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World—0
Neighboring Backdoor Attacks on Graph Convolutional Network—0
Defending against Backdoor Attacks in Natural Language Generation—0
Rethink the Evaluation for Attack Strength of Backdoor Attacks in Natural Language Processing—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
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