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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 411–420 of 492 papers

TitleStatusHype
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
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