  Background: When using deep learning models, there are many possible
vulnerabilities and some of the most worrying are the adversarial inputs, which
can cause wrong decisions with minor perturbations. Therefore, it becomes
necessary to retrain these models against adversarial inputs, as part of the
software testing process addressing the vulnerability to these inputs.
Furthermore, for an energy efficient testing and retraining, data scientists
need support on which are the best guidance metrics and optimal dataset
configurations.
  Aims: We examined four guidance metrics for retraining convolutional neural
networks and three retraining configurations. Our goal is to improve the models
against adversarial inputs regarding accuracy, resource utilization and time
from the point of view of a data scientist in the context of image
classification.
  Method: We conducted an empirical study in two datasets for image
classification. We explore: (a) the accuracy, resource utilization and time of
retraining convolutional neural networks by ordering new training set by four
different guidance metrics (neuron coverage, likelihood-based surprise
adequacy, distance-based surprise adequacy and random), (b) the accuracy and
resource utilization of retraining convolutional neural networks with three
different configurations (from scratch and augmented dataset, using weights and
augmented dataset, and using weights and only adversarial inputs).
  Results: We reveal that retraining with adversarial inputs from original
weights and by ordering with surprise adequacy metrics gives the best model
w.r.t. the used metrics.
  Conclusions: Although more studies are necessary, we recommend data
scientists to use the above configuration and metrics to deal with the
vulnerability to adversarial inputs of deep learning models, as they can
improve their models against adversarial inputs without using many inputs.
