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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 291–300 of 523 papers

TitleStatusHype
Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning—0
Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks—0
TrojanRobot: Physical-World Backdoor Attacks Against VLM-based Robotic Manipulation—0
TrojVLM: Backdoor Attack Against Vision Language Models—0
Understanding Impacts of Task Similarity on Backdoor Attack and Detection—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning—0
Unlearn to Relearn Backdoors: Deferred Backdoor Functionality Attacks on Deep Learning Models—0
UOR: Universal Backdoor Attacks on Pre-trained Language Models—0
VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation—0
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