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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 341–350 of 492 papers

TitleStatusHype
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
Adversarial Vulnerability of Active Transfer Learning—0
A Framework of Randomized Selection Based Certified Defenses Against Data Poisoning Attacks—0
A GAN-based data poisoning framework against anomaly detection in vertical federated learning—0
A Geometric Approach to Problems in Optimization and Data Science—0
A Gradient Method for Multilevel Optimization—0
A Linear Approach to Data Poisoning—0
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters—0
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