Publications

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In Press
W. W. Y. Ng, Hu, J., Yeung, D., Yin, S., and Roli, F., “Diversified Sensitivity based Undersampling for Imbalance Classification Problems”, IEEE Transactions on Cybernetics, In Press. (1.91 MB)
Y. Guan, Li, C. - T., and 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)
2023
C. Di Ruberto, Loddo, A., and Putzu, L., “On The Potential of Image Moments for Medical Diagnosis”, Journal of Imaging, vol. 9, 2023.
2022
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)
R. Delussu, Putzu, L., and 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, pp. 1208 – 1214.
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. Meloni, Sanna, A., Maiorca, D., and 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). pp. 42-52, 2022. (328.32 KB)
F. Crecchi, Melis, M., Sotgiu, A., Bacciu, D., and Biggio, B., “FADER: Fast adversarial example rejection”, Neurocomputing, vol. 470, pp. 257-268, 2022.
A. Janovsky, Maiorca, D., Marko, D., Matyas, V., and Giacinto, G., “A Longitudinal Study of Cryptographic API: A Decade of Android Malware”, 19th International Conference on Security and Cryptography (SECRYPT). pp. 121-133, 2022. (251.06 KB)
L. Borzacchiello, Coppa, E., Maiorca, D., Columbu, A., Demetrescu, C., and 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 and 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., and 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., and Biggio, B., “secml: A Python Library for Secure and Explainable Machine Learning”, SoftwareX, 2022.
C. Di Ruberto, Loddo, A., and 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., and Sperduti, A., “Towards learning trustworthily, automatically, and with guarantees on graphs: An overview”, Neurocomputing, vol. 493, pp. 217-243, 2022.
2021
L. Demetrio, Coull, S. E., Biggio, B., Lagorio, G., Armando, A., and 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 and Biggio, B., “Adversarial Machine Learning: Attacks From Laboratories to the Real World”, Computer, vol. 54, pp. 56-60, 2021.
L. Putzu, Untesco, M., and Fumera, G., “Automatic Myelofibrosis Grading from Silver-Stained Images”, in Computer Analysis of Images and Patterns, Cham, 2021, pp. 195–205.
A. Loddo and Putzu, L., “On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario”, AI, vol. 2, pp. 394–412, 2021.
P. Temple, Perrouin, G., Acher, M., Biggio, B., Jézéquel, J. - M., and Roli, F., “Empirical Assessment of Generating Adversarial Configurations for Software Product Lines”, Empirical Software Engineering, vol. 26, no. 6, 2021. (1.29 MB)
M. Pintor, Roli, F., Brendel, W., and Biggio, B., “Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints”, in NeurIPS, 2021.
L. Demetrio, Biggio, B., Lagorio, G., Roli, F., and Armando, A., “Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware”, IEEE Transactions on Information Forensics and Security, vol. 16, pp. 3469-3478, 2021.
A. Emanuele Cinà, Vascon, S., Demontis, A., Biggio, B., Roli, F., and 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, pp. 1–8.
E. Ledda, Putzu, L., Delussu, R., Loddo, A., and Fumera, G., “How Realistic Should Synthetic Images Be for Training Crowd Counting Models?”, in Computer Analysis of Images and Patterns, Cham, 2021, pp. 46–56.

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