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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 341–350 of 523 papers

TitleStatusHype
Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack—0
MDTD: A Multi Domain Trojan Detector for Deep Neural NetworksCode0
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation—0
Temporal-Distributed Backdoor Attack Against Video Based Action Recognition—0
DFB: A Data-Free, Low-Budget, and High-Efficacy Clean-Label Backdoor AttackCode0
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers—0
Beating Backdoor Attack at Its Own GameCode0
Adversarial Feature Map Pruning for BackdoorCode0
Rethinking Backdoor Attacks—0
Attacking by Aligning: Clean-Label Backdoor Attacks on Object DetectionCode0
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