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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 321–330 of 523 papers

TitleStatusHype
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios—0
Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models—0
Prompt Backdoors in Visual Prompt Learning—0
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation—0
Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of Artificial Mental Imagery—0
Punctuation Matters! Stealthy Backdoor Attack for Language Models—0
QTrojan: A Circuit Backdoor Against Quantum Neural Networks—0
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning—0
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks—0
Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization—0
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