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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 111–120 of 523 papers

TitleStatusHype
DBA: Distributed Backdoor Attacks against Federated LearningCode1
Clean-Label Backdoor Attacks on Video Recognition ModelsCode1
Hidden Trigger Backdoor AttacksCode1
A new Backdoor Attack in CNNs by training set corruption without label poisoningCode1
VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation—0
Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning—0
CUBA: Controlled Untargeted Backdoor Attack against Deep Neural Networks—0
Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments—0
ME: Trigger Element Combination Backdoor Attack on Copyright Infringement—0
Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems—0
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