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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 451–460 of 523 papers

TitleStatusHype
Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain—0
Backdoor Attack and Defense for Deep Regression—0
Excess Capacity and Backdoor PoisoningCode0
Poison Ink: Robust and Invisible Backdoor AttackCode1
Rethinking Stealthiness of Backdoor Attack against NLP ModelsCode1
BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised LearningCode1
Can You Hear It? Backdoor Attacks via Ultrasonic Triggers—0
Subnet Replacement: Deployment-stage backdoor attack against deep neural networks in gray-box setting—0
BadNL: Backdoor Attacks Against NLP Models—0
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from ScratchCode1
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