| Semester | Summer 2023 |
| Course type | Lecture |
| Lecturer | TT.-Prof. Dr. Wressnegger |
| Audience | Informatik Master & Bachelor |
| Credits | 3 ECTS |
| Time | 15:45–17:15 |
| Room | -101 (50.34) |
| Language | English |
| Link | https://ilias.studium.kit.edu/goto.php?target=crs%5F2081072&client_id=produktiv |
| Registration | TBA (registration is only necessary for the final exam) |
This lecture explicitly focuses on the security of machine learning algorithms. In learning-based systems, often only average-case performances are considered to show the effectiveness of AI methods. Worse-case scenarios triggered by viciously crafted inputs, however, can be exploited by an adversary to cause devastating damage in the application area. It thus is of utmost importance to investigate, research, and know about the security properties of machine learning methods.
The module introduces students to theoretic and practical aspects of security of machine learning algorithms and methods. In the first part, we cover offensive aspects of the topic. We will learn about different attack types such as adversarial examples (both white-box and black-box) or data poisoning and explicitly address problem-space constraints. In the second part, we explicitly focus on defensive mechanisms, such as adversarial training and network pruning. Finally, we will also cover methods for explaining learning-based algorithms to assist analysis and securing of machine learning methods.
This year we are going to do this in a flipped class-room setting. The lecture contents are distributed via video recordings, in a way that you can learn at your own speed. Additionally, we are meeting up for discussions, Q&A, and exercises at university. This way, we hopefully get the best from both worlds.
News about the lecture, potential updates to the schedule, and additional material are distributed using the course's matrix room. Moreover, matrix enables students to discuss topics and solution approaches.
You find the link to the matrix room on ILIAS.