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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 176–200 of 523 papers

TitleStatusHype
Backdoor Attack with Mode Mixture Latent Modification—0
Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning—0
AS-FIBA: Adaptive Selective Frequency-Injection for Backdoor Attack on Deep Face Restoration—0
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning—0
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World—0
DarkMind: Latent Chain-of-Thought Backdoor in Customized LLMs—0
BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning—0
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning—0
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks—0
Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee—0
DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data—0
Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias—0
Contributor-Aware Defenses Against Adversarial Backdoor Attacks—0
Deep Learning Backdoors—0
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection—0
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation—0
DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints—0
Defending against Backdoor Attack on Deep Neural Networks—0
Defending Against Backdoor Attack on Graph Nerual Network by Explainability—0
Backdoor Attack with Imperceptible Input and Latent Modification—0
Confidence Matters: Inspecting Backdoors in Deep Neural Networks via Distribution Transfer—0
Defending against Backdoor Attacks in Natural Language Generation—0
Defending Against Backdoor Attacks Using Ensembles of Weak Learners—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System—0
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