SOTAVerified

Data Poisoning

Data Poisoning is an adversarial attack that tries to manipulate the training dataset in order to control the prediction behavior of a trained model such that the model will label malicious examples into a desired classes (e.g., labeling spam e-mails as safe).

Source: Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Papers

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Showing 401–425 of 492 papers

TitleStatusHype
Context is the Key: Backdoor Attacks for In-Context Learning with Vision Transformers—0
ControlNET: A Firewall for RAG-based LLM System—0
Concealed Data Poisoning Attacks on NLP Models—0
Cut the Deadwood Out: Post-Training Model Purification with Selective Module Substitution—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey—0
Data-Dependent Stability Analysis of Adversarial Training—0
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study—0
Data Poisoning against Differentially-Private Learners: Attacks and Defenses—0
Data Poisoning: An Overlooked Threat to Power Grid Resilience—0
Data Poisoning Attack against Unsupervised Node Embedding Methods—0
Data Poisoning Attacks against Online Learning—0
Data Poisoning Attacks and Defenses to Crowdsourcing Systems—0
Data Poisoning Attacks in Contextual Bandits—0
Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems—0
Data Poisoning Attacks on Factorization-Based Collaborative Filtering—0
Data Poisoning Attacks on Federated Machine Learning—0
Data Poisoning Attacks on Neighborhood-based Recommender Systems—0
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods—0
Data Poisoning Attacks on Stochastic Bandits—0
Data Poisoning Attacks to Deep Learning Based Recommender Systems—0
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols—0
Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks—0
Data Poisoning Won’t Save You From Facial Recognition—0
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses—0
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