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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 201–210 of 492 papers

TitleStatusHype
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks—0
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy—0
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning—0
Fed-Credit: Robust Federated Learning with Credibility Management—0
Federated Unlearning—0
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning—0
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks—0
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