Adversarial Machine Learning


SemesterWinter 2022
Course typeBlock Seminar
LecturerJun.-Prof. Dr. Wressnegger
AudienceInformatik Master & Bachelor
Credits4 ECTS
Room148, Building 50.34
LanguageEnglish or German


This seminar is concerned with different aspects of adversarial machine learning. Next to the use of machine learning for security, also the security of machine learning algorithms is essential in practice. For a long time, machine learning has not considered worst-case scenarios and corner cases as those exploited by an adversarial nowadays.

The module introduces students to the recently extremely active field of attacks against machine learning and teaches them to work up results from recent research. To this end, the students will read up on a sub-field, prepare a seminar report, and present their work at the end of the term to their colleagues.

Topics include but are not limited to adversarial examples, model stealing, membership inferences, poisoning attacks, and defenses against such threats.


Tue, 25. Oct, 9:45–11:15Primer on academic writing, assignment of topics
Thu, 3. NovArrange appointment with assistant
Mon, 7. Nov - Fri, 11. Nov1st individual meeting (First overview, ToC)
Mo, 5. Dec - Fri, 9. Dec2nd individual meeting (Feedback on first draft of the report)
Thu, 22. DecSubmit final paper
Mon, 9. JanSubmit review for fellow students
Thu, 12. JanEnd of discussion phase
Fri, 13. JanNotification about paper acceptance/rejection
Fri, 27. JanSubmit camera-ready version of your paper
Fri, 17. FebPresentation at final colloquium

Mailing List

News about the seminar, potential updates to the schedule, and additional material are distributed using a separate mailing list. Moreover, the list enables students to discuss topics of the seminar.

You can subscribe here.


Every student may choose one of the following topics. For each of these, we additionally provide a few recent top-tier publication that you should use as a starting point for your own research. For the seminar and your final report, you should not merely summarize that paper, but try to go beyond and arrive at your own conclusions.

Moreover, most of these papers come with open-source implementations. Play around with these and include the lessons learned in your report.

  • TBA