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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 501–523 of 523 papers

TitleStatusHype
Defending against Backdoors in Federated Learning with Robust Learning RateCode1
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural NetworksCode1
Natural Backdoor Attack on Text Data—0
Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?Code1
Graph BackdoorCode1
Backdoor Attacks to Graph Neural NetworksCode1
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements—0
Adversarial examples are useful too!Code0
DBA: Distributed Backdoor Attacks against Federated LearningCode1
Rethinking the Trigger of Backdoor Attack—0
Dynamic Backdoor Attacks Against Machine Learning Models—0
Clean-Label Backdoor Attacks on Video Recognition ModelsCode1
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing—0
Defending against Backdoor Attack on Deep Neural Networks—0
Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks—0
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations—0
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy—0
Defending Neural Backdoors via Generative Distribution ModelingCode0
Hidden Trigger Backdoor AttacksCode1
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks—0
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural NetworksCode0
A new Backdoor Attack in CNNs by training set corruption without label poisoningCode1
Backdooring Convolutional Neural Networks via Targeted Weight Perturbations—0
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