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Franziska Roesner
Researcher at University of Washington
Publications - Â 97
Citations - Â 7178
Franziska Roesner is an academic researcher from University of Washington. The author has contributed to research in topics: Augmented reality & Computer science. The author has an hindex of 30, co-authored 82 publications receiving 5761 citations. Previous affiliations of Franziska Roesner include University of Texas at Austin & University of Texas System.
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Proceedings ArticleDOI
Experimental Security Analysis of a Modern Automobile
Karl Koscher,Alexei Czeskis,Franziska Roesner,Shwetak N. Patel,Tadayoshi Kohno,Stephen Checkoway,Damon McCoy,Brian Kantor,Danny Anderson,Hovav Shacham,Stefan Savage +10 more
TL;DR: It is demonstrated that an attacker who is able to infiltrate virtually any Electronic Control Unit (ECU) can leverage this ability to completely circumvent a broad array of safety-critical systems and present composite attacks that leverage individual weaknesses.
Proceedings Article
Comprehensive experimental analyses of automotive attack surfaces
Stephen Checkoway,Damon McCoy,Brian Kantor,Danny Anderson,Hovav Shacham,Stefan Savage,Karl Koscher,Alexei Czeskis,Franziska Roesner,Tadayoshi Kohno +9 more
TL;DR: This work discovers that remote exploitation is feasible via a broad range of attack vectors (including mechanics tools, CD players, Bluetooth and cellular radio), and further, that wireless communications channels allow long distance vehicle control, location tracking, in-cabin audio exfiltration and theft.
Proceedings Article
Defending Against Neural Fake News
Rowan Zellers,Ari Holtzman,Hannah Rashkin,Yonatan Bisk,Ali Farhadi,Franziska Roesner,Yejin Choi +6 more
TL;DR: A model for controllable text generation called Grover, found that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data, and the best defense against Grover turns out to be Grover itself, with 92% accuracy.
Proceedings Article
Detecting and defending against third-party tracking on the web
TL;DR: This work develops a client-side method for detecting and classifying five kinds of third-party trackers based on how they manipulate browser state, and finds that no existing browser mechanisms prevent tracking by social media sites via widgets while still allowing those widgets to achieve their utility goals, which leads to a new defense.
Proceedings ArticleDOI
User-Driven Access Control: Rethinking Permission Granting in Modern Operating Systems
TL;DR: This paper takes the approach of user-driven access control, whereby permission granting is built into existing user actions in the context of an application, rather than added as an afterthought via manifests or system prompts.