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)
R. Delussu, Putzu, L., and Fumera, G., “Scene-specific Crowd Counting Using Synthetic Training Images”, Pattern Recognition, In Press. (3.14 MB)
2022
F. Crecchi, Melis, M., Sotgiu, A., Bacciu, D., and Biggio, B., “FADER: Fast adversarial example rejection”, Neurocomputing, vol. 470, pp. 257-268, 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.
L. Putzu, Loddo, A., and 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, pp. 287–297.
D. Solans, Biggio, B., and Castillo, C., “Poisoning Attacks on Algorithmic Fairness”, in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020), 2021, p. 162--177. (1.05 MB)
M. Kravchik, Biggio, B., and 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, pp. 116–125.
2020
D. Maiorca, Demontis, A., Biggio, B., Roli, F., and Giacinto, G., “Adversarial Detection of Flash Malware: Limitations and Open Issues”, Computers & Security, vol. 96, 2020. (1.08 MB)
L. Putzu, Piras, L., and 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, pp. 26995-27021, 2020.
A. Sotgiu, Demontis, A., Melis, M., Biggio, B., Fumera, G., Feng, X., and Roli, F., “Deep Neural Rejection against Adversarial Examples”, EURASIP Journal on Information Security, vol. 5, 2020.
C. Di Ruberto, Loddo, A., and 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., and 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, pp. 373-380. (527.29 KB)
L. Putzu and Fumera, G., “An empirical evaluation of nuclei segmentation from H&E images in a real application scenario”, Applied Sciences (Switzerland), vol. 10, pp. 1-15, 2020.
F. Cara, Scalas, M., Giacinto, G., and Maiorca, D., “On the Feasibility of Adversarial Sample Creation Using the Android System API”, Information, no. 11(9): 433, 2020. (1.26 MB)
R. Soleymani, Granger, E., and Fumera, G., “F-Measure Curves: A Tool to Visualize Classifier Performance Under Imbalance”, Pattern Recognition, vol. 100, p. 107146, 2020. (3.15 MB)

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