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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 121–130 of 492 papers

TitleStatusHype
Data-Dependent Stability Analysis of Adversarial Training—0
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study—0
A BIC-based Mixture Model Defense against Data Poisoning Attacks on Classifiers—0
Data Poisoning: An Overlooked Threat to Power Grid Resilience—0
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods—0
Data Poisoning Attacks to Deep Learning Based Recommender Systems—0
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols—0
Data Poisoning Won’t Save You From Facial Recognition—0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
Cascading Adversarial Bias from Injection to Distillation in Language Models—0
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