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Book Chapter
G. Fumera, Marcialis, G. L., Biggio, B., Roli, F., e Schuckers, S. C., «Multimodal Anti-Spoofing in Biometric Recognition Systems», in Handbook of Biometric Anti-Spoofing, S. Marcel, Nixon, M., e Li, S. Z. Springer, 2014, pagg 165-184. (155.83 KB)
B. Biggio, Corona, I., Nelson, B., Rubinstein, B. I. P., Maiorca, D., Fumera, G., Giacinto, G., e Roli, F., «Security Evaluation of Support Vector Machines in Adversarial Environments», in Support Vector Machines Applications, Y. Ma e Guo, G. Springer International Publishing, 2014, pagg 105-153. (687.1 KB)
Conference Paper
B. Biggio, Corona, I., Maiorca, D., Nelson, B., Srndic, N., Laskov, P., Giacinto, G., e Roli, F., «Evasion attacks against machine learning at test time», in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2013, vol 8190, pagg 387-402. (473.78 KB)
G. L. Marcialis e Roli, F., «Experimental results on fusion of multiple fingerprint matchers», in 4th International Conference on Audio- and Video-based Person Authentication (AVBPA03), Guildford (U.K.), 2003, vol 2688, pagg 814-820.
G. L. Marcialis, Roli, F., e Serrau, A., «Fusion of Statistical and Structural Fingerprint Classifiers», in 4th Internation Audio- and Video-Based Person Authentication, Guildford, 2003, vol 2688, pagg 310-317.
M. Jagielski, Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., e Li, B., «Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning», in 39th IEEE Symposium on Security and Privacy, 2018. (1.02 MB)
B. Nelson, Biggio, B., e Laskov, P., «Microbagging Estimators: An Ensemble Approach to Distance-weighted Classifiers», in Journal of Machine Learning Research - Proc. 3rd Asian Conference on Machine Learning (ACML 2011), Taoyuan, Taiwan, 2011, vol 20, pagg 63-79. (481.46 KB)
B. Biggio, Nelson, B., e Laskov, P., «Poisoning attacks against support vector machines», in 29th Int'l Conf. on Machine Learning (ICML), 2012, pagg 1807–1814. (452.94 KB)
I. Sanchez, Satta, R., Nai-Fovino, I., Baldini, G., Steri, G., Shaw, D., e Ciardulli, A., «Privacy leakages in Smart Home Wireless Technologies», in 2014 IEEE International Carnahan Conference on Security Technology, Rome, Italy, 2014. (329.97 KB)
B. Biggio, Nelson, B., e Laskov, P., «Support Vector Machines Under Adversarial Label Noise», in Journal of Machine Learning Research - Proc. 3rd Asian Conference on Machine Learning (ACML 2011), Taoyuan, Taiwan, 2011, vol 20, pagg 97-112. (533.74 KB)
B. Nelson, Biggio, B., e Laskov, P., «Understanding the Risk Factors of Learning in Adversarial Environments», in 4th ACM Workshop on Artificial Intelligence and Security (AISec 2011), Chicago, IL, USA, 2011, pagg 87–92. (132.42 KB)
A. Demontis, Melis, M., Pintor, M., Jagielski, M., Biggio, B., Oprea, A., Nita-Rotaru, C., e Roli, F., «Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks», in 28th Usenix Security Symposium, Santa Clara, California, USA, 2019, vol 28th {USENIX} Security Symposium ({USENIX} Security 19), pag 321--338. (1.09 MB)
Journal Article
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)
M. Narouei, Ahmadi, M., Giacinto, G., Takabi, H., e Sami, A., «DLLMiner: structural mining for malware detection», Security and Communication Networks, 2015. (731.78 KB)
H. Xiao, Biggio, B., Nelson, B., Xiao, H., Eckert, C., e Roli, F., «Support Vector Machines under Adversarial Label Contamination», Neurocomputing, Special Issue on Advances in Learning with Label Noise, vol 160, pagg 53-62, 2015. (2.8 MB)