@inproceedings {pub3790pub3889,
	title = {Recovering Localized Adversarial Attacks},
	author = {Jan Philip Goepfert AND Barbara Hammer AND Heiko Wersing},
	year = {2019},
	month = {September},
	abstract = {Classification algorithms can achieve greatly improved per-
formance when given the option to reject samples. In fact, knowing when
the prediction for a given sample is too uncertain can provide valuable
feedback to a system{\textquoteright}s users. In order for users to better understand and
judge a system{\textquoteright}s capabilities, and be able to adequatly respond to rejected
samples, we need to be able to explain why a given sample leads to a
prediction with low certainty. To this end, we look at different classifi-
cation algorithms, namely knn and a Convolutional Neural Network, and
investigate how they respond to different inputs, which include typical,
atypical, unclear, and adverserial samples. We use LIME to approximate
and understand not just the classification itself, but the corresponding
certainty. With this understanding of c y we then propose ways of
communication the newly gained knowledge to users.},
	publisher = {Springer, LNCS, LNAI, LNBI},
	booktitle = {International Conference on Artificial Neural Networks ICANN},
	pages = {302-311}
}