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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
Fed-Credit: Robust Federated Learning with Credibility Management—0
Defending against Backdoor Attack on Deep Neural Networks—0
Defend Data Poisoning Attacks on Voice Authentication—0
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning—0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks—0
Federated Multi-Armed Bandits Under Byzantine Attacks—0
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application—0
Federated Unlearning—0
Deep Probabilistic Models to Detect Data Poisoning Attacks—0
Deep Learning Model Security: Threats and Defenses—0
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