Konrad Rieck is a professor at TU Berlin, where he leads the Chair of Machine Learning and Security as part of the Berlin Institute for the Foundations of Learning and Data. Previously, he held academic positions at TU Braunschweig, the University of Göttingen, and Fraunhofer Institute FIRST. His research focuses on the intersection of computer security and machine learning. He has published over 100 papers in this area and serves on the PCs of the top security conferences (system security circus). He has been awarded the CAST/GI Dissertation Award, a Google Faculty Award, and an ERC Consolidator Grant.
The number of papers submitted to scientific conferences is steadily rising in many disciplines. To handle this growth, systems for automatic paper-reviewer assignments are increasingly used during the reviewing process. These systems employ statistical topic models to characterize the papers' content and automate their assignment to reviewers. In this talk, we invesitgate the security of this automation and introduce a new attack that modifies a given paper so that it selects its own reviewers. Our attack is based on a novel optimization strategy that fools the topic model with unobtrusive changes to the paper's content. In an empirical evaluation with a (simulated) conference, our attack successfully selects and removes reviewers, while the tampered papers remain plausible and often indistinguishable from innocuous submissions.