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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

Showing 501510 of 523 papers

TitleStatusHype
SSDA: Secure Source-Free Domain AdaptationCode0
DFB: A Data-Free, Low-Budget, and High-Efficacy Clean-Label Backdoor AttackCode0
Energy Backdoor Attack to Deep Neural NetworksCode0
Whispers in Grammars: Injecting Covert Backdoors to Compromise Dense Retrieval SystemsCode0
Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated LearningCode0
EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding InspectionCode0
Versatile Weight Attack via Flipping Limited BitsCode0
Efficient Backdoor Attacks for Deep Neural Networks in Real-world ScenariosCode0
Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion ModelsCode0
TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language ModelsCode0
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