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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 401–410 of 523 papers

TitleStatusHype
Be Careful with Rotation: A Uniform Backdoor Pattern for 3D Shape—0
Behavior Backdoor for Deep Learning Models—0
Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning—0
BFClass: A Backdoor-free Text Classification Framework—0
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning—0
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy—0
C^2 ATTACK: Towards Representation Backdoor on CLIP via Concept Confusion—0
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers—0
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models—0
CBPF: Filtering Poisoned Data Based on Composite Backdoor Attack—0
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