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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
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios—0
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation—0
Backdoor Attacks on the DNN Interpretation System—0
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks—0
Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks—0
BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models—0
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems—0
Deep Learning Backdoors—0
Natural Backdoor Attack on Text Data—0
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