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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 421–430 of 523 papers

TitleStatusHype
A Knowledge Distillation-Based Backdoor Attack in Federated Learning—0
FRIB: Low-poisoning Rate Invisible Backdoor Attack based on Feature Repair—0
Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment—0
Versatile Weight Attack via Flipping Limited BitsCode0
Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain—0
Backdoor Attack is a Devil in Federated GAN-based Medical Image SynthesisCode0
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning—0
Defending Backdoor Attacks on Vision Transformer via Patch Processing—0
Transferable Graph Backdoor Attack—0
Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection—0
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