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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
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems—0
Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce—0
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms—0
Swallowing the Poison Pills: Insights from Vulnerability Disparity Among LLMs—0
Sybil-based Virtual Data Poisoning Attacks in Federated Learning—0
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers—0
Systematic Testing of the Data-Poisoning Robustness of KNN—0
Targeted Data Poisoning Attack on News Recommendation System by Content Perturbation—0
Targeted Data Poisoning for Black-Box Audio Datasets Ownership Verification—0
A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning—0
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