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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 81–90 of 492 papers

TitleStatusHype
Adversarial Vulnerability of Active Transfer Learning—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks—0
Backdoor Attack and Defense for Deep Regression—0
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing—0
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
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