Biblio

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2022
Bieringer L, Grosse K, Backes M, Biggio B, Krombholz K.  2022.  Industrial practitioners' mental models of adversarial machine learning. Eighteenth Symposium on Usable Privacy and Security (SOUPS 2022). :97--116.
Zhang Z, Chen M, Backes M, Shen Y, Zhang Y.  2022.  Inference Attacks Against Graph Neural Networks. 31st USENIX Security Symposium (USENIX Security 22). :4543--4560.
Liu Y, Wen R, He X, Salem A, Zhang Z, Backes M, De Cristofaro E, Fritz M, Zhang Y.  2022.  ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models. 31st USENIX Security Symposium (USENIX Security 22). :4525--4542.
2015
Backes M, Bugiel S, Hammer C, Schranz O, von Styp-Rekowsky P.  2015.  Boxify: Full-fledged App Sandboxing for Stock Android. 24th USENIX Security Symposium (USENIX Security 15). :691--706.
2014
Backes M, Nürnberger S.  2014.  Oxymoron: Making Fine-Grained Memory Randomization Practical by Allowing Code Sharing. 23rd USENIX Security Symposium (USENIX Security 14). :433--447.