| Semester | Winter 2020 |
| Course type | Block Seminar |
| Lecturer | TT.-Prof. Dr. Wressnegger |
| Audience | Informatik Master & Bachelor |
| Credits | 4 ECTS |
| Room | 148, Building 50.34 and online |
| Language | English or German |
| Link | https://campus.kit.edu/campus/lecturer/event.asp?gguid=0x1602699F5FBA4AE5AFF64437C3FA2CA2 |
| Registration | https://ilias.studium.kit.edu/goto_produktiv_crs_1265039.html |
Due to the ongoing COVID-19 pandemic, this course is going to start off remotely, meaning, the kick-off meeting will happen online. The final colloquium, however, will hopefully be an in-person meeting again.
To receive all the necessary information, please subscribe to the mailing list here.
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.
| Date | Step |
| Tue, 3. Nov, 09:45–11:15 | Primer on academic writing, assignment of topics |
| Thu, 12. Nov | Arrange appointment with assistant |
| Mo, 16. Nov - Fr, 20. Nov | Individual meetings with assistant |
| Wed, 16. Dec | Submit final paper |
| Wed, 20. Jan | Submit review for fellow students |
| Fri, 22. Jan | End of discussion phase |
| Fri, 29. Jan | Submit camera-ready version of your paper |
| Fri, 12. Feb | Presentation at final colloquium |
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 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, all of these papers come with open-source implementations. Play around with these and include the lessons learned in your report.
Effective Writing Style Transfer via Combinatorial Paraphrasing, PoPETS 2020
Understanding and Preventing Image-Scaling Attacks in Machine Learning, USENIX Security 2020
Leveraging Frequency Analysis for Deep Fake Image Recognition, ICML 2020
Intriguing Properties of Adversarial ML Attacks in the Problem Space, IEEE S&P 2020
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems, IEEE S&P 2021
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference, USENIX Security 2020
Using Honeypots to Catch Adversarial Attacks on Neural Networks, CCS 2020
Fast is better than free: Revisiting adversarial training, ICLR 2020