  In the field of high-energy physics, deep learning algorithms continue to
gain in relevance and provide performance improvements over traditional
methods, for example when identifying rare signals or finding complex patterns.
From an analyst's perspective, obtaining highest possible performance is
desirable, but recently, some attention has been shifted towards studying
robustness of models to investigate how well these perform under slight
distortions of input features. Especially for tasks that involve many
(low-level) inputs, the application of deep neural networks brings new
challenges. In the context of jet flavor tagging, adversarial attacks are used
to probe a typical classifier's vulnerability and can be understood as a model
for systematic uncertainties. A corresponding defense strategy, adversarial
training, improves robustness, while maintaining high performance.
Investigating the loss surface corresponding to the inputs and models in
question reveals geometric interpretations of robustness, taking correlations
into account.
