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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 191–200 of 492 papers

TitleStatusHype
Fed-Credit: Robust Federated Learning with Credibility Management—0
Explainable Label-flipping Attacks on Human Emotion Assessment System—0
A Robust Attack: Displacement Backdoor Attack—0
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks—0
Exploring Vulnerabilities and Protections in Large Language Models: A Survey—0
Face Recognition in the age of CLIP & Billion image datasets—0
Fairness-aware Summarization for Justified Decision-Making—0
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning—0
Defending Against Backdoor Attacks Using Ensembles of Weak Learners—0
Defending against Backdoor Attack on Deep Neural Networks—0
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