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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 381–390 of 492 papers

TitleStatusHype
BrainWash: A Poisoning Attack to Forget in Continual Learning—0
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models—0
Breaking Fair Binary Classification with Optimal Flipping Attacks—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Balancing Privacy, Robustness, and Efficiency in Machine Learning—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Cascading Adversarial Bias from Injection to Distillation in Language Models—0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks—0
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing—0
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