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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 221–230 of 523 papers

TitleStatusHype
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks—0
BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records—0
Backdoor Attacks with Input-unique Triggers in NLP—0
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models—0
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only—0
EmoAttack: Utilizing Emotional Voice Conversion for Speech Backdoor Attacks on Deep Speech Classification Models—0
A4O: All Trigger for One sample—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
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