@inproceedings{46331, abstract = {{Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually - been rather difficult.In this work, we introduce a method for generating strong adversaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results.}}, author = {{Seiler, Moritz Vinzent and Trautmann, Heike and Kerschke, Pascal}}, booktitle = {{Proceedings of the International Joint Conference on Neural Networks (IJCNN)}}, pages = {{1–8}}, title = {{{Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries}}}, doi = {{10.1109/IJCNN48605.2020.9207338}}, year = {{2020}}, } @misc{18639, author = {{Terfort, Tobias}}, publisher = {{Universität Paderborn}}, title = {{{Enhancing Security by Usage of Universal One-Way Hash Functions}}}, year = {{2020}}, } @article{28341, author = {{Grimminger-Seidensticker, Elke and Möhwald, Aiko Julia}}, issn = {{1740-8989}}, journal = {{Physical Education and Sport Pedagogy}}, number = {{3}}, pages = {{316--329}}, title = {{{Enhancing social cohesion in PE classes within an intercultural learning program: results of a quasi-experimental intervention study}}}, doi = {{10.1080/17408989.2020.1741532}}, volume = {{25}}, year = {{2020}}, } @inbook{37491, author = {{Haak, Inka and Gildehaus, Lara and Liebendörfer, Michael}}, booktitle = {{Beiträge zum Mathematikunterricht 2020}}, editor = {{Siller, Hans-Stefan and Weigel, Wolfgang and Wörler, Jan Franz}}, pages = {{1405–1408}}, publisher = {{WTM-Verlag}}, title = {{{Entstehung und Bedeutung von Lerngruppen in der Studieneingangsphase}}}, year = {{2020}}, } @phdthesis{42755, author = {{Pietsch, Tommy}}, isbn = {{978-3-8440-7128-3}}, title = {{{Entwicklung des Prägeelementschweißens für Aluminium-Stahl-Verbindungen im Karosseriebau}}}, year = {{2020}}, } @inproceedings{24012, author = {{Kullmer, Gunter and Weiß, Deborah and Bauer, Benjamin and Richard, Hans Albert}}, location = {{Hamburg}}, pages = {{61--70}}, title = {{{Entwicklung einer Axialrissprobe zur Ermittlung von bruchmechanischen Kennwerten für Rohre}}}, volume = {{DVM-Bericht 252}}, year = {{2020}}, } @phdthesis{42758, author = {{Gerkens, Michael}}, isbn = {{978-3-8440-7583-0}}, title = {{{Entwicklung einer Methodik zur numerischen Simulation des Hochgeschwindigkeits-Bolzensetzens}}}, year = {{2020}}, } @article{24234, author = {{Moritzer, Elmar and Krassmann, Dimitri}}, journal = {{Joining Plastics}}, pages = {{96--103}}, title = {{{Entwicklung einer neuartigen Fügetechnik für Organoblech-Hybridverbindungen}}}, year = {{2020}}, } @phdthesis{27646, author = {{Resonnek, Verena}}, title = {{{Entwicklung einer Zylindertemperatureinstellungsregelung auf Basis von Fuzzy-Logik }}}, year = {{2020}}, } @article{24956, author = {{Bauer, Anna and Reinhold, Peter and Sacher, Marc}}, journal = {{Phydid B, Didaktik der Physik, Beiträge zur DPG-Frühjahrstagung}}, pages = {{389--396}}, title = {{{Entwicklung eines Bewertungsmodells zur handlungsorientierten Messung experimenteller Kompetenz (Physik)Studierender}}}, year = {{2020}}, }