Adversarial training

Adversarial training is an approach to machine learning which seeks to increase the model’s robustness by arming it with the capability to defend against attack. It is a form of data augmentation that introduces a form of artificial noise into the training data. Such noise can be generated by algorithms, or simply by adding small perturbations to the input samples.

The goal of adversarial training is to make the model’s performance more robust to certain types of attack. For example, adding small perturbations to the input data may make the model less sensitive to certain types of attack. This is similar in principle to the concept of regularization, in which the model is trained to have less reliance on specific features.

Adversarial training can help to reduce the risk of overfitting, as the noisy data encourages the model to generalize more effectively. It can also be used to increase the accuracy of the model on new datasets, as the robustness to attack improves its performance.

Adversarial training may also help to improve the security of the model itself, as it has a higher degree of resistance to certain attacks. This makes it less vulnerable to manipulation or poisoning of the training data, for example, which can result in poor performance.

Adversarial training is commonly used to enhance the performance and security of deep learning models. It has been used to increase the accuracy and robustness of data-driven approaches to cybersecurity, such as anomaly detection and network intrusion prevention.