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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 131–140 of 523 papers

TitleStatusHype
Effective backdoor attack on graph neural networks in link prediction tasks—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection—0
Data-centric NLP Backdoor Defense from the Lens of Memorization—0
Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving—0
BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records—0
An Effective and Resilient Backdoor Attack Framework against Deep Neural Networks and Vision Transformers—0
BadApex: Backdoor Attack Based on Adaptive Optimization Mechanism of Black-box Large Language Models—0
Backdoor Attack against NLP models with Robustness-Aware Perturbation defense—0
Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks—0
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