Publications

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A. Demontis, Melis, M., Biggio, B., Maiorca, D., Arp, D., Rieck, K., Corona, I., Giacinto, G., and Roli, F., “Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection”, IEEE Trans. Dependable and Secure Computing, vol. 16, no. 4, pp. 711-724, 2019. (3.61 MB)
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B. Biggio and Roli, F., “Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning”, Pattern Recognition, vol. 84, pp. 317-331, 2018. (3.76 MB)
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)
D. M. Freeman, Jain, S., Duermuth, M., Biggio, B., and 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., 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)
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P. Temple, Acher, M., Perrouin, G., Biggio, B., Jezequel, J. - M., and 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, pp. 277–288. (2.09 MB)
L. Muñoz-González, Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., and Roli, F., “Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization”, in 10th ACM Workshop on Artificial Intelligence and Security, 2017, pp. 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., and Sperduti, A., “Towards learning trustworthily, automatically, and with guarantees on graphs: An overview”, Neurocomputing, vol. 493, pp. 217-243, 2022.
D. Maiorca, Biggio, B., and Giacinto, G., “Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks”, ACM Computing Surveys, vol. 52, no. 4, 2019. (1.21 MB)
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B. Biggio, Fumera, G., Pillai, I., and Roli, F., “A survey and experimental evaluation of image spam filtering techniques”, Pattern Recognition Letters, vol. 32, pp. 1436 - 1446, 2011. (2.12 MB)
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)
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)
A. Demontis, Biggio, B., Fumera, G., and 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), pp. 551-562. (678.7 KB)
A. Demontis, Melis, M., Biggio, B., Fumera, G., and Roli, F., “Super-sparse Learning in Similarity Spaces”, IEEE Computational Intelligence Magazine, vol. 11, no. 4, pp. 36-45, 2016. (555.22 KB)
B. Biggio, Fumera, G., Marcialis, G. L., and Roli, F., “Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 3, pp. 561-575, 2017. (5.7 MB)
B. Biggio, Melis, M., Fumera, G., and Roli, F., “Sparse Support Faces”, in Int'l Conf. on Biometrics (ICB), 2015, pp. 208-213. (702.84 KB)
B. Biggio, Fumera, G., Marcialis, G. L., and 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., 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)
B. Biggio, Fumera, G., and Roli, F., “Security evaluation of pattern classifiers under attack”, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 4, pp. 984-996, 2014. (1.35 MB)
B. Biggio, Akhtar, Z., Fumera, G., Marcialis, G. L., and Roli, F., “Security evaluation of biometric authentication systems under real spoofing attacks”, IET Biometrics, vol. 1, no. 1, pp. 11-24, 2012. (3.21 MB)
A. Demontis, Russu, P., Biggio, B., Fumera, G., and 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, pp. 322-332. (425.68 KB)
P. Russu, Demontis, A., Biggio, B., Fumera, G., and Roli, F., “Secure Kernel Machines against Evasion Attacks”, in 9th ACM Workshop on Artificial Intelligence and Security, Vienna, Austria, 2016, pp. 59-69. (686.41 KB)
M. Pintor, Demetrio, L., Sotgiu, A., Melis, M., Demontis, A., and 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., and 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., and 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, pp. 5-10. (954 KB)

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