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Article summary:
1. This article reviews poisoning attacks that can compromise the trustworthiness of machine-learning models.
2. It discusses how to mitigate these attacks before, during, and after model training.
3. It concludes by formulating open challenges which are hindering the development of testing methods and benchmarks for assessing and improving the trustworthiness of machine-learning models against data poisoning attacks.
Article analysis:
The article is written in a clear and concise manner, providing an overview of the current state of machine learning security against data poisoning attacks. The authors provide a comprehensive review of existing poisoning attacks and discuss potential mitigation strategies for each attack type. The article also outlines some relevant open challenges which are hindering the development of testing methods and benchmarks suitable for assessing and improving the trustworthiness of machine-learning models against data poisoning attacks.
The article is generally reliable, however there are some points that could be further explored or clarified. For example, while the authors provide an overview of existing poisoning attacks, they do not discuss any potential countermeasures or solutions to these attacks in detail. Additionally, while the authors mention some open challenges related to testing methods and benchmarks for assessing trustworthiness against data poisoning attacks, they do not provide any specific suggestions on how these challenges could be addressed or overcome.
In conclusion, this article provides a comprehensive overview of machine learning security against data poisoning attacks but could benefit from further exploration into potential countermeasures or solutions to these types of attack as well as more detailed suggestions on how to address open challenges related to testing methods and benchmarks for assessing trustworthiness against data poisoning attacks.