Faculty
Mario Fritz is a faculty member at the CISPA Helmholtz Center for Information Security, an honorary professor at Saarland University, and a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). Until 2018, he led a research group at the Max Planck Institute for Computer Science. Previously, he was a PostDoc at the International Computer Science Institute (ICSI) and UC Berkeley after receiving his PhD from TU Darmstadt and studying computer science at FAU Erlangen-Nuremberg. His research focuses on trustworthy artificial intelligence, especially at the intersection of information security and machine learning. He is Associate Editor of the journal "IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)," coordinates the Helmholtz project "Trustworthy Federated Data Analytics," and has published over 100 scientific articles - 80 of them in top conferences and journals.
PETS
Proceedings on Privacy Enhancing TechnologiesPrivacy Enhancing Technologies
AISec
AISec '21: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security14th ACM Workshop on Artificial Intelligence and Security, co-located with the 28th ACM Conference on Computer and Communications Security
UNSPECIFIED
MTD '21: Proceedings of the 8th ACM Workshop on Moving Target DefenseMoving Target Defense Workshop in conjuncture with CCS
ICCV
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)IEEE International Conference on Computer Vision (ICCV) 2021
ICCV
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)IEEE International Conference on Computer Vision (ICCV), 2021
UNSPECIFIED
Proceedings of the 1st International Workshop on Machine Learning in Software EngineeringInternational Workshop on Machine Learning in Software Engineering in conjunction with ECML PKDD
UNSPECIFIED
Proceedings of the 1st International Workshop on Machine Learning in Software EngineeringInternational Workshop on Machine Learning in Software Engineering in conjuncture with ECML PKDD
UNSPECIFIED
SampleFix: Learning to Generate Functionally Diverse Fixes1st International Workshop on Machine Learning in Software Engineering
International Journal of Computer Vision (IJCV)
IJCAI
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-2130th International Joint Conference on Artificial Intelligence (IJCAI)
Trustworthy Machine Learning
Machine learning has made great advances over the past year and many techniques have found their ways into applications. This leads to an increasing demand of techniques that not only perform well - but are also "trustworthy".
Trustworthiness includes:
- Interpretability of the prediction
- Robustness against changes to the input, which occur naturally or with malicious intend
- Privacy preserving machine learning (e.g. when dealing with sensitive data such as in health applications)
- Fairness
- ...
As a proseminar’s primary purpose is to learn presentation skills, the seminar will feature two presentations from each student.
In the first half of the semester, we will have presentations of two topics each week. After each presentation, fellow students and lecturers will provide feedback on how to improve the presentation. This general feedback must then be taken into account for the second half of the semester, where again each student will present.
The first presentations will count towards 30% of the overall grade, the second presentation will count towards 70% of the overall grade. Attendance in the proseminar meetings is mandatory. At most one session can be skipped, after that you need to bring a doctor’s note to excuse your absence.
Machine Learning in Cyber Security
Recent advances in Machine Learning has lead to near (or beyond) human-level performance in many tasks - autonomous driving, voice assistance, playing a variety of games. In terms of privacy and security, this is a double-edged sword. ML techniques can be used to efficiently detect and prevent attacks (e.g., intrusion detection). However, their deployment to many real-world sensitive systems (e.g., self-driving cars, the cloud) also makes them susceptible to numerous attacks, such as introducing imperceptible perturbations in inputs and forcing ML systems behave in unintended ways.
The course explores in-depth both of these sides to Machine Learning and Cyber Security. The content addresses the following areas:
While we do a brief recap in the beginning, the course requires knowledge on Machine Learning.
Date for lecture: Tuesdays noon to 2pm.
Date for exercise: Fridays 2pm to 4pm
Due to the size of the course - the lecture will start in an online format until further notice.
The course requires prior knowledge on Machine Learning.
Once you have registered - please find internal information and schedule and links here (under construction).
Proseminar: Trustworthy Machine Learning
Machine learning has made great advances over the past year and many techniques have found their ways into applications. This leads to an increasing demand of techniques that not only perform well - but are also "trustworthy".
Lecture: High Level Computer Vision
This course will cover essential techniques for high-level computer vision. These techniques facilitate semantic interpretation of visual data, as it is required for a broad range of applications like robotics, driver assistance, multi-media retrieval, surveillance etc.
Lecture: Machine Learning in Cybersecurity
Recent advances in Machine Learning has lead to near (or beyond) human-level performance in many tasks - autonomous driving, voice assistance, playing a variety of games.