M. Melis, Scalas, M., Demontis, A., Maiorca, D., Biggio, B., Giacinto, G., and Roli, F.,
“Do Gradient-Based Explanations Tell Anything About Adversarial Robustness to Android Malware?”,
International Journal of Machine Learning and Cybernetics, vol. 13, pp. 217-232, 2022.
(1.2 MB) E. Ledda, Putzu, L., Delussu, R., Fumera, G., and Roli, F.,
“On the Evaluation of Video-Based Crowd Counting Models”,
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13233 LNCS. pp. 301 – 311, 2022.
A. Sotgiu, Pintor, M., and Biggio, B.,
“Explainability-Based Debugging of Machine Learning for Vulnerability Discovery”, in
Proc. 17th International Conference on Availability, Reliability and Security, New York, NY, USA, 2022.
F. Crecchi, Melis, M., Sotgiu, A., Bacciu, D., and Biggio, B.,
“FADER: Fast adversarial example rejection”,
Neurocomputing, vol. 470, pp. 257-268, 2022.