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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
Enhancing Clean Label Backdoor Attack with Two-phase Specific Triggers—0
A temporal chrominance trigger for clean-label backdoor attack against anti-spoof rebroadcast detection—0
BadDet: Backdoor Attacks on Object DetectionCode0
Contributor-Aware Defenses Against Adversarial Backdoor Attacks—0
BagFlip: A Certified Defense against Data PoisoningCode0
BITE: Textual Backdoor Attacks with Iterative Trigger InjectionCode0
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning—0
Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution—0
Model-Contrastive Learning for Backdoor DefenseCode0
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning—0
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