SOTAVerified

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 301–350 of 523 papers

TitleStatusHype
Multi-Target Federated Backdoor Attack Based on Feature Aggregation—0
Natural Backdoor Attack on Text Data—0
Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving—0
Neighboring Backdoor Attacks on Graph Convolutional Network—0
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations—0
Object-oriented backdoor attack against image captioning—0
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing—0
On Feasibility of Server-side Backdoor Attacks on Split Learning—0
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models—0
PAD-FT: A Lightweight Defense for Backdoor Attacks via Data Purification and Fine-Tuning—0
Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models—0
Partial train and isolate, mitigate backdoor attack—0
PBSM: Backdoor attack against Keyword spotting based on pitch boosting and sound masking—0
Physical Invisible Backdoor Based on Camera Imaging—0
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks—0
PointBA: Towards Backdoor Attacks in 3D Point Cloud—0
Poisoning-based Backdoor Attacks for Arbitrary Target Label with Positive Triggers—0
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds—0
Poison in the Well: Feature Embedding Disruption in Backdoor Attacks—0
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models—0
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios—0
Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models—0
Prompt Backdoors in Visual Prompt Learning—0
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation—0
Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of Artificial Mental Imagery—0
Punctuation Matters! Stealthy Backdoor Attack for Language Models—0
QTrojan: A Circuit Backdoor Against Quantum Neural Networks—0
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning—0
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks—0
Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization—0
Rethinking Backdoor Attacks—0
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective—0
Rethinking the Trigger-injecting Position in Graph Backdoor Attack—0
Rethinking the Trigger of Backdoor Attack—0
Rethink the Evaluation for Attack Strength of Backdoor Attacks in Natural Language Processing—0
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models—0
Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift—0
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks—0
Robo-Troj: Attacking LLM-based Task Planners—0
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy—0
Robust Backdoor Attacks against Deep Neural Networks in Real Physical World—0
Robust Backdoor Attacks on Object Detection in Real World—0
Versatile Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers—0
SAB:A Stealing and Robust Backdoor Attack based on Steganographic Algorithm against Federated Learning—0
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning—0
SATBA: An Invisible Backdoor Attack Based On Spatial Attention—0
Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments—0
Securing Federated Learning against Backdoor Threats with Foundation Model Integration—0
Manipulating and Mitigating Generative Model Biases without Retraining—0
SFIBA: Spatial-based Full-target Invisible Backdoor Attacks—0
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