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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 171–180 of 523 papers

TitleStatusHype
Towards Robust Physical-world Backdoor Attacks on Lane Detection—0
Poisoning-based Backdoor Attacks for Arbitrary Target Label with Positive Triggers—0
BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection—0
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning—0
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning—0
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction—0
LSP Framework: A Compensatory Model for Defeating Trigger Reverse Engineering via Label Smoothing Poisoning—0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only—0
Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving—0
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