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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 371–380 of 492 papers

TitleStatusHype
Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification—0
Reinforcement Learning For Data Poisoning on Graph Neural Networks—0
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors—0
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models—0
Property Inference From Poisoning—0
Adversarial Vulnerability of Active Transfer Learning—0
Data Poisoning Attacks to Deep Learning Based Recommender Systems—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Sself: Robust Federated Learning against Stragglers and Adversaries—0
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