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
F. Crecchi, Melis, M., Sotgiu, A., Bacciu, D., e Biggio, B., «FADER: Fast adversarial example rejection», Neurocomputing, vol 470, pagg 257-268, 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.
L. Putzu, Loddo, A., e Di Ruberto, C., «Invariant Moments, Textural and Deep Features for Diagnostic MR and CT Image Retrieval», in Computer Analysis of Images and Patterns, Cham, 2021, pagg 287–297.
D. Solans, Biggio, B., e Castillo, C., «Poisoning Attacks on Algorithmic Fairness», in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020), 2021, pag 162--177. (1.05 MB)
M. Kravchik, Biggio, B., e Shabtai, A., «Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems», in Proceedings of the 36th Annual ACM Symposium on Applied Computing, New York, NY, USA, 2021, pagg 116–125.
2020
D. Maiorca, Demontis, A., Biggio, B., Roli, F., e Giacinto, G., «Adversarial Detection of Flash Malware: Limitations and Open Issues», Computers & Security, vol 96, 2020. (1.08 MB)
L. Putzu, Piras, L., e Giacinto, G., «Convolutional neural networks for relevance feedback in content based image retrieval: A Content based image retrieval system that exploits convolutional neural networks both for feature extraction and for relevance feedback», Multimedia Tools and Applications, vol 79, pagg 26995-27021, 2020.
A. Sotgiu, Demontis, A., Melis, M., Biggio, B., Fumera, G., Feng, X., e Roli, F., «Deep Neural Rejection against Adversarial Examples», EURASIP Journal on Information Security, vol 5, 2020.
C. Di Ruberto, Loddo, A., e Putzu, L., «Detection of red and white blood cells from microscopic blood images using a region proposal approach», Computers in Biology and Medicine, vol 116, 2020.
R. Delussu, Putzu, L., e Fumera, G., «An Empirical Evaluation of Cross-scene Crowd Counting Performance», in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISAPP, Valletta - Malta, 2020, vol 4, pagg 373-380. (527.29 KB)
L. Putzu e Fumera, G., «An empirical evaluation of nuclei segmentation from H&E images in a real application scenario», Applied Sciences (Switzerland), vol 10, pagg 1-15, 2020.
F. Cara, Scalas, M., Giacinto, G., e Maiorca, D., «On the Feasibility of Adversarial Sample Creation Using the Android System API», Information, n° 11(9): 433, 2020. (1.26 MB)
R. Soleymani, Granger, E., e Fumera, G., «F-Measure Curves: A Tool to Visualize Classifier Performance Under Imbalance», Pattern Recognition, vol 100, pag 107146, 2020. (3.15 MB)
R. Delussu, Putzu, L., e Fumera, G., «Investigating Synthetic Data Sets for Crowd Counting in Cross-scene Scenarios», in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISAPP 2020, Valletta - Malta, 2020, vol 4, pagg 365-372. (4.23 MB)
R. Delussu, Putzu, L., Fumera, G., e Roli, F., «Online Domain Adaptation for Person Re-Identification with a Human in the Loop», in 25th International Conference on Pattern Recognition, {ICPR} 2020, Virtual Event / Milan, Italy, January 10-15, 2021, 2020, pagg 3829–3836. (770.02 KB)

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