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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 141–150 of 523 papers

TitleStatusHype
Strategic Planning of Stealthy Backdoor Attacks in Markov Decision Processes—0
Exploring Backdoor Attack and Defense for LLM-empowered Recommendations—0
Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models—0
ShadowCoT: Cognitive Hijacking for Stealthy Reasoning Backdoors in LLMs—0
Backdoor Detection through Replicated Execution of Outsourced Training—0
A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction—0
DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data—0
Towards Invisible Backdoor Attack on Text-to-Image Diffusion ModelCode0
A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks—0
Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks—0
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