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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 491–500 of 523 papers

TitleStatusHype
Stealthy Backdoors as Compression ArtifactsCode0
Robust Backdoor Attacks against Deep Neural Networks in Real Physical World—0
Explainability-based Backdoor Attacks Against Graph Neural Networks—0
Backdoor Attack in the Physical World—0
PointBA: Towards Backdoor Attacks in 3D Point Cloud—0
EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry—0
Hidden Backdoor Attack against Semantic Segmentation Models—0
Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models—0
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection—0
BAAAN: Backdoor Attacks Against Auto-encoder and GAN-Based Machine Learning Models—0
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