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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 371–380 of 492 papers

TitleStatusHype
Data Poisoning to Fake a Nash Equilibrium in Markov Games—0
Online Data Poisoning Attack—0
Online Data Poisoning Attacks—0
On Optimal Learning Under Targeted Data Poisoning—0
On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks—0
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning—0
On the Effectiveness of Poisoning against Unsupervised Domain Adaptation—0
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models—0
On the Relevance of Byzantine Robust Optimization Against Data Poisoning—0
On the Robustness of Graph Reduction Against GNN Backdoor—0
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