We present here the main research topics and activities on security, safety, and robustness of machine learning models developed at the Pattern Recognition and Applications (PRA) Laboratory of the University of Cagliari. We have provided pioneering contributions to this research area, being the first to demonstrate gradient-based attacks to craft adversarial examples and training data poisoning attacks. The findings of our research have significantly contributed not only to identifying and characterizing vulnerabilities of such models in the context of real-world applications but also to the development of more trustworthy artificial intelligence and machine learning models. We are part of the ELSA network of excellence for the development of safe and secure AI-based technologies, funded by the European Union.

AI Security and Safety: The PRALab Research Experience

Ambra Demontis;Maura Pintor;Angelo Sotgiu;Daniele Angioni;Giorgio Piras;Srishti Gupta;Battista Biggio;Fabio Roli
2023-01-01

Abstract

We present here the main research topics and activities on security, safety, and robustness of machine learning models developed at the Pattern Recognition and Applications (PRA) Laboratory of the University of Cagliari. We have provided pioneering contributions to this research area, being the first to demonstrate gradient-based attacks to craft adversarial examples and training data poisoning attacks. The findings of our research have significantly contributed not only to identifying and characterizing vulnerabilities of such models in the context of real-world applications but also to the development of more trustworthy artificial intelligence and machine learning models. We are part of the ELSA network of excellence for the development of safe and secure AI-based technologies, funded by the European Union.
2023
Artificial Intelligence; Security, Safety; Adversarial Machine Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/377243
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