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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 241–250 of 523 papers

TitleStatusHype
EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry—0
Fake the Real: Backdoor Attack on Deep Speech Classification via Voice Conversion—0
EmoAttack: Utilizing Emotional Voice Conversion for Speech Backdoor Attacks on Deep Speech Classification Models—0
EmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation—0
Feature Grinding: Efficient Backdoor Sanitation in Deep Neural Networks—0
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models—0
Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection—0
Effective backdoor attack on graph neural networks in link prediction tasks—0
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