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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 241–250 of 523 papers

TitleStatusHype
Evolutionary Trigger Detection and Lightweight Model Repair Based Backdoor Defense—0
BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records—0
Backdoor Graph CondensationCode0
SOS! Soft Prompt Attack Against Open-Source Large Language Models—0
Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated LearningCode0
Attack On Prompt: Backdoor Attack in Prompt-Based Continual Learning—0
Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift—0
CBPF: Filtering Poisoned Data Based on Composite Backdoor Attack—0
EmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation—0
Backdooring Bias into Text-to-Image ModelsCode0
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