What is model poisoning?
Best insight from top research papers
Model poisoning refers to the act of injecting a small amount of corrupted training data into a machine learning model in order to decrease its test accuracy. It is a type of attack that aims to compromise the performance of the model by training it on incorrect data. Existing attacks on model poisoning have been relatively ineffective against modern machine learning architectures. However, recent research has explored the limits of data poisoning attacks and identified a threshold, known as the poisoning ratio, which determines the effectiveness of these attacks in achieving certain target parameters. Various techniques and algorithms have been proposed to perform model poisoning attacks, including parameter corruption attacks and the Gradient Canceling attack. Additionally, defense mechanisms, such as diversified ensembles of classifiers and data cleaning methods, have been developed to mitigate the impact of model poisoning attacks.
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| Papers (5) | Insight |
|---|---|
30 May 2022 | Model poisoning refers to the act of injecting maliciously mislabeled training data into the training pipeline of a machine learning model in order to degrade its accuracy. |
06 Mar 2023 | Model poisoning refers to the act of injecting a small amount of corrupted training data into a machine learning model in order to decrease its test accuracy. |
01 Jun 2021 4 Citations | Model poisoning is the act of injecting a small fraction of poisoning instances into the training data used to train a machine learning model in order to compromise its performance. |
| Model poisoning refers to the act of injecting a small amount of corrupted training data into a machine learning model in order to decrease its test accuracy. | |
| Model poisoning is a type of attack in federated learning where malicious participants manipulate their uploaded model parameters to manipulate system outputs. |
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