Publications

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A. Demontis, Biggio, B., Fumera, G., e Roli, F., «Super-Sparse Regression for Fast Age Estimation From Faces at Test Time», in 18th Int'l Conf. on Image Analysis and Processing (ICIAP), Genova, Italy, 2015, vol Image Analysis and Processing (ICIAP 2015), pagg 551-562. (678.7 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)
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. Biggio, Fumera, G., Pillai, I., e Roli, F., «A survey and experimental evaluation of image spam filtering techniques», Pattern Recognition Letters, vol 32, pagg 1436 - 1446, 2011. (2.12 MB)
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D. Maiorca, Biggio, B., e Giacinto, G., «Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks», ACM Computing Surveys, vol 52, n° 4, 2019. (1.21 MB)
L. Muñoz-González, Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., e Roli, F., «Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization», in 10th ACM Workshop on Artificial Intelligence and Security, 2017, pagg 27-38. (4.08 MB)
P. Temple, Acher, M., Perrouin, G., Biggio, B., Jezequel, J. - M., e Roli, F., «Towards Quality Assurance of Software Product Lines with Adversarial Configurations», in Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A, New York, NY, USA, 2019, pagg 277–288. (2.09 MB)
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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)
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D. M. Freeman, Jain, S., Duermuth, M., Biggio, B., e Giacinto, G., «Who Are You? A Statistical Approach to Measuring User Authenticity», in Proc. 23rd Annual Network & Distributed System Security Symposium (NDSS), 2016. (764.14 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)
B. Biggio e Roli, F., «Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning», Pattern Recognition, vol 84, pagg 317-331, 2018. (3.76 MB)

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