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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 291–300 of 492 papers

TitleStatusHype
Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems—0
Temporal Robustness against Data Poisoning—0
Run-Off Election: Improved Provable Defense against Data Poisoning AttacksCode0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
Face Recognition in the age of CLIP & Billion image datasets—0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application—0
Computation and Data Efficient Backdoor Attacks—0
Defending Against Disinformation Attacks in Open-Domain Question AnsweringCode0
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning—0
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