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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 281–290 of 523 papers

TitleStatusHype
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
ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger—0
Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain—0
Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
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