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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 101–150 of 523 papers

TitleStatusHype
ONION: A Simple and Effective Defense Against Textual Backdoor AttacksCode1
Backdoor Attack against Speaker VerificationCode1
Embedding and Extraction of Knowledge in Tree Ensemble ClassifiersCode1
Input-Aware Dynamic Backdoor AttackCode1
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free CasesCode1
Defending against Backdoors in Federated Learning with Robust Learning RateCode1
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural NetworksCode1
Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?Code1
Graph BackdoorCode1
Backdoor Attacks to Graph Neural NetworksCode1
DBA: Distributed Backdoor Attacks against Federated LearningCode1
Clean-Label Backdoor Attacks on Video Recognition ModelsCode1
Hidden Trigger Backdoor AttacksCode1
A new Backdoor Attack in CNNs by training set corruption without label poisoningCode1
VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation—0
Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning—0
CUBA: Controlled Untargeted Backdoor Attack against Deep Neural Networks—0
Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments—0
ME: Trigger Element Combination Backdoor Attack on Copyright Infringement—0
Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems—0
SPBA: Utilizing Speech Large Language Model for Backdoor Attacks on Speech Classification Models—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Invisible Backdoor Triggers in Image Editing Model via Deep WatermarkingCode0
Heterogeneous Graph Backdoor Attack—0
Poison in the Well: Feature Embedding Disruption in Backdoor Attacks—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization—0
FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition—0
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning—0
MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schrödinger BridgesCode0
Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving—0
BadLingual: A Novel Lingual-Backdoor Attack against Large Language Models—0
Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF FingerprintingCode0
Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion ModelsCode0
SFIBA: Spatial-based Full-target Invisible Backdoor Attacks—0
Erased but Not Forgotten: How Backdoors Compromise Concept Erasure—0
BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts—0
Robo-Troj: Attacking LLM-based Task Planners—0
BadApex: Backdoor Attack Based on Adaptive Optimization Mechanism of Black-box Large Language Models—0
Strategic Planning of Stealthy Backdoor Attacks in Markov Decision Processes—0
Exploring Backdoor Attack and Defense for LLM-empowered Recommendations—0
Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models—0
ShadowCoT: Cognitive Hijacking for Stealthy Reasoning Backdoors in LLMs—0
Backdoor Detection through Replicated Execution of Outsourced Training—0
A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction—0
DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data—0
Towards Invisible Backdoor Attack on Text-to-Image Diffusion ModelCode0
A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks—0
Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks—0
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