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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 276–300 of 523 papers

TitleStatusHype
Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks—0
TARGET: Template-Transferable Backdoor Attack Against Prompt-based NLP Models via GPT4—0
Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment—0
Temporal-Distributed Backdoor Attack Against Video Based Action Recognition—0
TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks—0
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks—0
The Art of Deception: Robust Backdoor Attack using Dynamic Stacking of Triggers—0
The last Dance : Robust backdoor attack via diffusion models and bayesian approach—0
The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipeline—0
Towards Robust Physical-world Backdoor Attacks on Lane Detection—0
Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger—0
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization—0
Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning—0
Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach—0
Transferable Graph Backdoor Attack—0
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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