Publications

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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.

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