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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 501–510 of 523 papers

TitleStatusHype
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
Long-Tailed Backdoor Attack Using Dynamic Data Augmentation Operations—0
Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm—0
Low-Loss Subspace Compression for Clean Gains against Multi-Agent Backdoor Attacks—0
LSP Framework: A Compensatory Model for Defeating Trigger Reverse Engineering via Label Smoothing Poisoning—0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning—0
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