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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 331–340 of 523 papers

TitleStatusHype
Rethinking Backdoor Attacks—0
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective—0
Rethinking the Trigger-injecting Position in Graph Backdoor Attack—0
Rethinking the Trigger of Backdoor Attack—0
Rethink the Evaluation for Attack Strength of Backdoor Attacks in Natural Language Processing—0
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models—0
Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift—0
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks—0
Robo-Troj: Attacking LLM-based Task Planners—0
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy—0
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