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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 331–340 of 492 papers

TitleStatusHype
ABC-FL: Anomalous and Benign client Classification in Federated Learning—0
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation—0
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles—0
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners—0
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems—0
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems—0
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
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