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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 361–370 of 523 papers

TitleStatusHype
Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class—0
An Embarrassingly Simple Backdoor Attack on Self-supervised LearningCode1
Understanding Impacts of Task Similarity on Backdoor Attack and Detection—0
Few-shot Backdoor Attacks via Neural Tangent KernelsCode0
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning DatasetsCode1
Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text ClassificationsCode0
Defending Against Backdoor Attack on Graph Nerual Network by Explainability—0
TrojViT: Trojan Insertion in Vision TransformersCode1
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
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