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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 461–470 of 492 papers

TitleStatusHype
Poisoning Attacks with Generative Adversarial NetsCode0
Mixed Strategy Game Model Against Data Poisoning Attacks—0
An Investigation of Data Poisoning Defenses for Online Learning—0
Data Poisoning Attacks on Stochastic Bandits—0
Robust Federated Training via Collaborative Machine Teaching using Trusted Instances—0
Data Poisoning Attack against Knowledge Graph Embedding—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
Data Poisoning against Differentially-Private Learners: Attacks and Defenses—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
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