Adversarial Machine Learning is a field of Machine Learning that focuses on exploiting model vulnerabilities by making use of obtainable information from the model. Studying a model’s weaknesses to adversarial attacks not only helps the researcher understand more about the model itself, but also allows them to defend against malicious attacks and prevent potentially fatal consequences after deployment. Adversarial Machine Learning was firstly proposed in the image classification domain, where an attack fools a model to misclassify an image by adding carefully crafted noise that is hardly detectable by a human. Recently, adversarial methods have been introduced that target time series challenges. We will develop and evaluate new adversarial attacks on time series, targeting specific time series challenges beyond forecasting.
Recommended skills: Basic knowledge of machine learning and python