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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 331–340 of 523 papers

TitleStatusHype
Backdoor Attacks in Peer-to-Peer Federated Learning—0
On the Vulnerability of Backdoor Defenses for Federated LearningCode1
BEAGLE: Forensics of Deep Learning Backdoor Attack for Better DefenseCode1
Universal Detection of Backdoor Attacks via Density-based Clustering and Centroids AnalysisCode0
Silent Killer: A Stealthy, Clean-Label, Black-Box Backdoor AttackCode1
Backdoor Attacks Against Dataset DistillationCode1
SSDA: Secure Source-Free Domain AdaptationCode0
You Are Catching My Attention: Are Vision Transformers Bad Learners Under Backdoor Attacks?—0
Color Backdoor: A Robust Poisoning Attack in Color SpaceCode0
Mind Your Heart: Stealthy Backdoor Attack on Dynamic Deep Neural Network in Edge ComputingCode0
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