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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 131–140 of 492 papers

TitleStatusHype
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
Cascading Adversarial Bias from Injection to Distillation in Language Models—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Balancing Privacy, Robustness, and Efficiency in Machine Learning—0
Approaching the Harm of Gradient Attacks While Only Flipping Labels—0
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Breaking Fair Binary Classification with Optimal Flipping Attacks—0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data—0
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