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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 381–390 of 523 papers

TitleStatusHype
BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection—0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT—0
BadHMP: Backdoor Attack against Human Motion Prediction—0
BadLingual: A Novel Lingual-Backdoor Attack against Large Language Models—0
BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts—0
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements—0
BadNL: Backdoor Attacks Against NLP Models—0
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models—0
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