Publications

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A. Demontis, Melis, M., Biggio, B., Maiorca, D., Arp, D., Rieck, K., Corona, I., Giacinto, G., e Roli, F., «Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection», IEEE Trans. Dependable and Secure Computing, vol 16, n° 4, pagg 711-724, 2019. (3.61 MB)
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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)
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)
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)
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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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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)
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)
L. Oneto, Navarin, N., Biggio, B., Errica, F., Micheli, A., Scarselli, F., Bianchini, M., Demetrio, L., Bongini, P., Tacchella, A., e Sperduti, A., «Towards learning trustworthily, automatically, and with guarantees on graphs: An overview», Neurocomputing, vol 493, pagg 217-243, 2022.
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)
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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)
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)
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)
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)
A. Demontis, Melis, M., Biggio, B., Fumera, G., e Roli, F., «Super-sparse Learning in Similarity Spaces», IEEE Computational Intelligence Magazine, vol 11, n° 4, pagg 36-45, 2016. (555.22 KB)
B. Biggio, Fumera, G., Marcialis, G. L., e Roli, F., «Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems», IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 39, n° 3, pagg 561-575, 2017. (5.7 MB)
B. Biggio, Melis, M., Fumera, G., e Roli, F., «Sparse Support Faces», in Int'l Conf. on Biometrics (ICB), 2015, pagg 208-213. (702.84 KB)
B. Biggio, Fumera, G., Marcialis, G. L., e Roli, F., «Security of pattern recognition systems in adversarial environments». 2012. (235.41 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)
B. Biggio, Fumera, G., e Roli, F., «Security evaluation of pattern classifiers under attack», IEEE Transactions on Knowledge and Data Engineering, vol 26, n° 4, pagg 984-996, 2014. (1.35 MB)
B. Biggio, Akhtar, Z., Fumera, G., Marcialis, G. L., e Roli, F., «Security evaluation of biometric authentication systems under real spoofing attacks», IET Biometrics, vol 1, n° 1, pagg 11-24, 2012. (3.21 MB)
A. Demontis, Russu, P., Biggio, B., Fumera, G., e Roli, F., «On Security and Sparsity of Linear Classifiers for Adversarial Settings», in Joint IAPR Int'l Workshop on Structural, Syntactic, and Statistical Pattern Recognition, Merida, Mexico, 2016, vol 10029 of LNCS, pagg 322-332. (425.68 KB)
P. Russu, Demontis, A., Biggio, B., Fumera, G., e Roli, F., «Secure Kernel Machines against Evasion Attacks», in 9th ACM Workshop on Artificial Intelligence and Security, Vienna, Austria, 2016, pagg 59-69. (686.41 KB)
M. Pintor, Demetrio, L., Sotgiu, A., Melis, M., Demontis, A., e Biggio, B., «secml: A Python Library for Secure and Explainable Machine Learning», SoftwareX, 2022.
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B. Biggio, Akhtar, Z., Fumera, G., Marcialis, G. L., e Roli, F., «Robustness of multi-modal biometric verification systems under realistic spoofing attacks», in Int’l Joint Conference on Biometrics (IJCB), Washington DC, USA, 2011. (2.25 MB)
Z. Akhtar, Biggio, B., Fumera, G., e Marcialis, G. L., «Robustness of Multi-modal Biometric Systems under Realistic Spoof Attacks against All Traits», in IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BioMS), Milan, Italy, 2011, pagg 5-10. (954 KB)

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