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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 121–130 of 523 papers

TitleStatusHype
Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Invisible Backdoor Triggers in Image Editing Model via Deep WatermarkingCode0
Heterogeneous Graph Backdoor Attack—0
Poison in the Well: Feature Embedding Disruption in Backdoor Attacks—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization—0
FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition—0
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning—0
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