2025W

• Bachelor Thesis / 192.061 / PR
• Machine Learning for Computer Security / 192.172 / VU
• Project in Computer Science 1 / 192.021 / PR
• Project in Computer Science 2 / 192.022 / PR
• Seminar for PhD Students / 192.060 / SE

2026S

• Project in Computer Science 1 / 192.021 / PR
• Project in Computer Science 2 / 192.022 / PR

• Building Robust and Explainable AI-based Defenses for Computer Security
  2024 – 2030 / Vienna Science and Technology Fund (WWTF)
  Publications: 222934 / 222191

• Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version] / Cortellazzi, J., Quiring, E., Arp, D., Pendlebury, F., Pierazzi, F., & Cavallaro, L. (2025). Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version]. ACM Transactions on Privacy and Security, 28(4), 1–37. https://doi.org/10.1145/3742895
  Project: BREADS (2024–2030)
• Seeing through: analyzing and attacking virtual backgrounds in video calls / Weißberg, F., Hilgefort, J. M., Grogorick, S., Arp, D., Eisenhofer, T., Eisemann, M., & Rieck, K. (2025). Seeing through: analyzing and attacking virtual backgrounds in video calls. In SEC ’25: Proceedings of the 34th USENIX Conference on Security Symposium (pp. 6561–6580). Association for Computing Machinery.
  Project: BREADS (2024–2030)
• Pitfalls in Machine Learning for Computer Security / Arp, D., Quiring, E., Pendlebury, F., Warnecke, A., Pierazzi, F., Wressnegger, C., Cavallaro, L., & Rieck, K. (2024). Pitfalls in Machine Learning for Computer Security. Communications of the ACM, 67(11), 104–112. https://doi.org/10.1145/3643456
  Download: Artikel (1.17 MB)