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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 141–150 of 492 papers

TitleStatusHype
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
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models—0
BrainWash: A Poisoning Attack to Forget in Continual Learning—0
An Optimal Control View of Adversarial Machine Learning—0
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems—0
ABC-FL: Anomalous and Benign client Classification in Federated Learning—0
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy—0
Blockchain for Large Language Model Security and Safety: A Holistic Survey—0
An Investigation of Data Poisoning Defenses for Online Learning—0
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