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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 291–300 of 523 papers

TitleStatusHype
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer—0
Contributor-Aware Defenses Against Adversarial Backdoor Attacks—0
Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee—0
Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning—0
CUBA: Controlled Untargeted Backdoor Attack against Deep Neural Networks—0
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning—0
Mitigating Backdoor Attack Via Prerequisite Transformation—0
Moiré Backdoor Attack (MBA): A Novel Trigger for Pedestrian Detectors in the Physical World—0
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