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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 341–350 of 523 papers

TitleStatusHype
A Spatiotemporal Stealthy Backdoor Attack against Cooperative Multi-Agent Deep Reinforcement Learning—0
A Survey on Backdoor Attack and Defense in Natural Language Processing—0
A temporal chrominance trigger for clean-label backdoor attack against anti-spoof rebroadcast detection—0
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning—0
BAAAN: Backdoor Attacks Against Auto-encoder and GAN-Based Machine Learning Models—0
BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models—0
Backdoor Attack against NLP models with Robustness-Aware Perturbation defense—0
Backdoor Attack Against Vision Transformers via Attention Gradient-Based Image Erosion—0
Backdoor Attack and Defense for Deep Regression—0
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis—0
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