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SIMONETTO Thibault Jean Angel

University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal

Main Referenced Co-authors
CORDY, Maxime  (2)
DYRMISHI, Salijona  (2)
GHAMIZI, Salah  (2)
LE TRAON, Yves  (2)
Main Referenced Keywords
Computer Vision: Adversarial learning, adversarial attack and defense methods (1); Constraint Satisfaction and Optimization: Constraint Optimization (1); Constraint Satisfaction and Optimization: Constraint Satisfaction (1); Constraint Satisfaction and Optimization: Constraints and Machine Learning (1); Search: Evolutionary Computation (1);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) (1)
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Other (1)
ULHPC - University of Luxembourg: High Performance Computing (1)
Main Referenced Disciplines
Computer science (2)

Publications (total 2)

The most downloaded
37 downloads
Simonetto, T. J. A., Dyrmishi, S., Ghamizi, S., Cordy, M., & Le Traon, Y. (2022). A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 (pp. 1313-1319). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2022/183 https://hdl.handle.net/10993/53045

Dyrmishi, S., Ghamizi, S., Simonetto, T. J. A., Le Traon, Y., & Cordy, M. (2023). On the empirical effectiveness of unrealistic adversarial hardening against realistic adversarial attacks. In Conference Proceedings 2023 IEEE Symposium on Security and Privacy (SP) (pp. 1384-1400). IEEE. doi:10.1109/SP46215.2023.00049
Peer reviewed

Simonetto, T. J. A., Dyrmishi, S., Ghamizi, S., Cordy, M., & Le Traon, Y. (2022). A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 (pp. 1313-1319). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2022/183
Peer reviewed

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