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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
Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection—0
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers—0
Krait: A Backdoor Attack Against Graph Prompt Tuning—0
LADDER: Multi-objective Backdoor Attack via Evolutionary Algorithm—0
Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks—0
LaserGuider: A Laser Based Physical Backdoor Attack against Deep Neural Networks—0
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning—0
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems—0
LoBAM: LoRA-Based Backdoor Attack on Model Merging—0
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