Publications

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Book Chapter
G. Fumera, Marcialis, G. L., Biggio, B., Roli, F., and Schuckers, S. C., Multimodal Anti-Spoofing in Biometric Recognition Systems, in Handbook of Biometric Anti-Spoofing, S. Marcel, Nixon, M., and Li, S. Z. Springer, 2014, pp. 165-184. (155.83 KB)
B. Biggio, Corona, I., Nelson, B., Rubinstein, B. I. P., Maiorca, D., Fumera, G., Giacinto, G., and Roli, F., Security Evaluation of Support Vector Machines in Adversarial Environments, in Support Vector Machines Applications, Y. Ma and Guo, G. Springer International Publishing, 2014, pp. 105-153. (687.1 KB)
Conference Paper
B. Biggio, Corona, I., Maiorca, D., Nelson, B., Srndic, N., Laskov, P., Giacinto, G., and 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, pp. 387-402. (473.78 KB)
G. L. Marcialis and 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, pp. 814-820.
G. L. Marcialis, Roli, F., and Serrau, A., Fusion of Statistical and Structural Fingerprint Classifiers, in 4th Internation Audio- and Video-Based Person Authentication, Guildford, 2003, vol. 2688, pp. 310-317.
V. - T. Ninh, Le, T. - K., Zhou, L., Piras, L., Riegler, M., Lux, M., Tran, M. - T., Gurrin, C., and Dang-Nguyen, D. - T., LIFER 2.0: Discovering Personal Lifelog Insights using an Interactive Lifelog Retrieval System, in Working Notes of {CLEF} 2019 - Conference and Labs of the Evaluation Forum, Lugano, Switzerland, September 9-12, 2019., 2019. (1.56 MB)
M. Jagielski, Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., and 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., and 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, pp. 63-79. (481.46 KB)
D. - T. Dang-Nguyen, Piras, L., Riegler, M., Zhou, L., Lux, M., Tran, M. - T., Le, T. - K., Ninh, V. - T., and Gurrin, C., Overview of ImageCLEFlifelog 2019: Solve My Life Puzzle and Lifelog Moment Retrieval, in Working Notes of {CLEF} 2019 - Conference and Labs of the Evaluation Forum, Lugano, Switzerland, September 9-12, 2019., 2019. (4.58 MB)
B. Biggio, Nelson, B., and Laskov, P., Poisoning attacks against support vector machines, in 29th Int'l Conf. on Machine Learning (ICML), 2012, pp. 1807–1814. (452.94 KB)
I. Sanchez, Satta, R., Nai-Fovino, I., Baldini, G., Steri, G., Shaw, D., and 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., and 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, pp. 97-112. (533.74 KB)
B. Nelson, Biggio, B., and 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, pp. 87–92. (132.42 KB)
A. Demontis, Melis, M., Pintor, M., Jagielski, M., Biggio, B., Oprea, A., Nita-Rotaru, C., and 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), p. 321--338. (1.09 MB)
Journal Article
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)
M. Narouei, Ahmadi, M., Giacinto, G., Takabi, H., and 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., and Roli, F., Support Vector Machines under Adversarial Label Contamination, Neurocomputing, Special Issue on Advances in Learning with Label Noise, vol. 160, pp. 53-62, 2015. (2.8 MB)