Attacks on machine learning models

Attacking Neural Networks is an article written by RNikhil which was published on January 7, 2024. The article examines various ways in which attackers can target neural networks (NNs). It begins by introducing the concept of NNs and how they are used to solve complex problems. It then goes on to discuss the different methods attackers use to exploit NNs, such as adversarial attacks, model poisoning, and data poisoning. Adversarial attacks involve introducing small changes to input data so that the output of the NN is incorrect or misleading. Model poisoning involves changing the internal structure of the NN in order to make predictions that are incorrect or misleading. Data poisoning involves corrupting training data in order to make the NN behave differently.

The article also discusses ways in which attackers can protect their own NNs from being targeted. These include increasing the complexity of the NN, using secure training architectures, and applying defense mechanisms such as adversarial training. Adversarial training is a method that is used to increase the robustness of a NN and make it more resilient to attack.

In conclusion, the article provides an overview of the different techniques used by attackers to target NNs and provides strategies for protecting NNs from these types of attack. By understanding the different methods and strategies available, developers can ensure their NNs are secure and protected from malicious actors.

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