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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 231–240 of 523 papers

TitleStatusHype
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only—0
A4O: All Trigger for One sample—0
Evil from Within: Machine Learning Backdoors through Hardware Trojans—0
Evolutionary Trigger Detection and Lightweight Model Repair Based Backdoor Defense—0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Explainability-based Backdoor Attacks Against Graph Neural Networks—0
Exploring Backdoor Attack and Defense for LLM-empowered Recommendations—0
Backdoor Attacks on the DNN Interpretation System—0
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