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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 321–330 of 523 papers

TitleStatusHype
Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks—0
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection—0
A Disguised Wolf Is More Harmful Than a Toothless Tiger: Adaptive Malicious Code Injection Backdoor Attack Leveraging User Behavior as Triggers—0
A Dual-Purpose Framework for Backdoor Defense and Backdoor Amplification in Diffusion Models—0
A Dual Stealthy Backdoor: From Both Spatial and Frequency Perspectives—0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective—0
Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning—0
A Master Key Backdoor for Universal Impersonation Attack against DNN-based Face Verification—0
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