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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 391–400 of 523 papers

TitleStatusHype
VSVC: Backdoor attack against Keyword Spotting based on Voiceprint Selection and Voice Conversion—0
Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks—0
WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks—0
Weak-to-Strong Backdoor Attack for Large Language Models—0
When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations—0
Widen The Backdoor To Let More Attackers In—0
You Are Catching My Attention: Are Vision Transformers Bad Learners Under Backdoor Attacks?—0
DeepBaR: Fault Backdoor Attack on Deep Neural Network Layers—0
Invisible Threats: Backdoor Attack in OCR Systems—0
Is It Possible to Backdoor Face Forgery Detection with Natural Triggers?—0
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