Publications

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In Press
W. W. Y. Ng, Hu, J., Yeung, D., Yin, S., e Roli, F., «Diversified Sensitivity based Undersampling for Imbalance Classification Problems», IEEE Transactions on Cybernetics, In Press. (1.91 MB)
Y. Guan, Li, C. - T., e Roli, F., «On Reducing the Effect of Covariate Factors in Gait Recognition: a Classifier Ensemble Method», IEEE Transactions on Pattern Analysis and Machine Intelligence, In Press. (311.43 KB) (151.4 KB)
2022
M. Melis, Scalas, M., Demontis, A., Maiorca, D., Biggio, B., Giacinto, G., e Roli, F., «Do Gradient-Based Explanations Tell Anything About Adversarial Robustness to Android Malware?», International Journal of Machine Learning and Cybernetics, vol 13, pagg 217-232, 2022. (1.2 MB)
R. Delussu, Putzu, L., e Fumera, G., «On the Effectiveness of Synthetic Data Sets for Training Person Re-identification Models», in Proceedings - International Conference on Pattern Recognition, 2022, vol 2022-August, pagg 1208 – 1214.
E. Ledda, Putzu, L., Delussu, R., Fumera, G., e 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. pagg 301 – 311, 2022.
A. Sotgiu, Pintor, M., e 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. Meloni, Sanna, A., Maiorca, D., e Giacinto, G., «Extended Abstract: Effective Call Graph Fingerprinting for the Analysis and Classification of Windows Malware», 19th Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA). pagg 42-52, 2022. (328.32 KB)
F. Crecchi, Melis, M., Sotgiu, A., Bacciu, D., e Biggio, B., «FADER: Fast adversarial example rejection», Neurocomputing, vol 470, pagg 257-268, 2022.
A. Janovsky, Maiorca, D., Marko, D., Matyas, V., e Giacinto, G., «A Longitudinal Study of Cryptographic API: A Decade of Android Malware», 19th International Conference on Security and Cryptography (SECRYPT). pagg 121-133, 2022. (251.06 KB)
L. Borzacchiello, Coppa, E., Maiorca, D., Columbu, A., Demetrescu, C., e Giacinto, G., «Reach Me if You Can: On Native Vulnerability Reachability in Android Apps», 27th European Symposium on Research in Computer Security (ESORICS). 2022. (979.51 KB)
A. Loddo e Putzu, L., «On the Reliability of CNNs in Clinical Practice: A Computer-Aided Diagnosis System Case Study», Applied Sciences (Switzerland), vol 12, 2022.
R. Delussu, Putzu, L., e Fumera, G., «Scene-specific Crowd Counting Using Synthetic Training Images», Pattern Recognition, vol 124, 2022. (3.14 MB)
M. Pintor, Demetrio, L., Sotgiu, A., Melis, M., Demontis, A., e Biggio, B., «secml: A Python Library for Secure and Explainable Machine Learning», SoftwareX, 2022.
C. Di Ruberto, Loddo, A., e Putzu, L., «Special Issue on Image Processing Techniques for Biomedical Applications», Applied Sciences (Switzerland), vol 12, 2022.
L. Oneto, Navarin, N., Biggio, B., Errica, F., Micheli, A., Scarselli, F., Bianchini, M., Demetrio, L., Bongini, P., Tacchella, A., e Sperduti, A., «Towards learning trustworthily, automatically, and with guarantees on graphs: An overview», Neurocomputing, vol 493, pagg 217-243, 2022.
2021
L. Demetrio, Coull, S. E., Biggio, B., Lagorio, G., Armando, A., e Roli, F., «Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection», ACM Trans. Priv. Secur., vol 24, 2021.
H. - Y. Lin e Biggio, B., «Adversarial Machine Learning: Attacks From Laboratories to the Real World», Computer, vol 54, pagg 56-60, 2021.
L. Putzu, Untesco, M., e Fumera, G., «Automatic Myelofibrosis Grading from Silver-Stained Images», in Computer Analysis of Images and Patterns, Cham, 2021, pagg 195–205.
A. Loddo e Putzu, L., «On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario», AI, vol 2, pagg 394–412, 2021.
P. Temple, Perrouin, G., Acher, M., Biggio, B., Jézéquel, J. - M., e Roli, F., «Empirical Assessment of Generating Adversarial Configurations for Software Product Lines», Empirical Software Engineering, vol 26, n° 6, 2021. (1.29 MB)
M. Pintor, Roli, F., Brendel, W., e Biggio, B., «Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints», in NeurIPS, 2021.
L. Demetrio, Biggio, B., Lagorio, G., Roli, F., e Armando, A., «Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware», IEEE Transactions on Information Forensics and Security, vol 16, pagg 3469-3478, 2021.
A. Emanuele Cinà, Vascon, S., Demontis, A., Biggio, B., Roli, F., e Pelillo, M., «The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?», in International Joint Conference on Neural Networks, (IJCNN) 2021, Shenzhen, China, 2021, pagg 1–8.
E. Ledda, Putzu, L., Delussu, R., Loddo, A., e Fumera, G., «How Realistic Should Synthetic Images Be for Training Crowd Counting Models?», in Computer Analysis of Images and Patterns, Cham, 2021, pagg 46–56.

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