Biggio, Battista

Poisoning Attacks against Support Vector Machines

Biggio, B., Nelson, B., & Laskov, P. (2012) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 1,331

Evasion Attacks against Machine Learning at Test Time

Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., & Roli, F. (2013) · Lecture Notes in Computer Science

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 890

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018) · 2018 IEEE Symposium on Security and Privacy (SP)

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 861

Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization

Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., & Roli, F. (2017) · Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 549

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

Cinà, A. E., Grosse, K., Demontis, A., Vascon, S., Zellinger, W., Moser, B. A., Oprea, A., Biggio, B., Pelillo, M., & Roli, F. (2023) · ACM Computing Surveys

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 152

Who Are You? A Statistical Approach to Measuring User Authenticity

Freeman, D., Jain, S., Duermuth, M., Biggio, B., & Giacinto, G. (2016) · Proceedings 2016 Network and Distributed System Security Symposium

User Authentication and Security SystemsSpam and Phishing Detection

Cited by 123

The Threat of Offensive AI to Organizations

Mirsky, Y., Demontis, A., Kotak, J., Shankar, R., Gelei, D., Yang, L., Zhang, X., Pintor, M., Lee, W., Elovici, Y., et al. (2023) · Computers & Security

Adversarial Robustness in Machine LearningInformation and Cyber Security

Cited by 97

One-and-a-Half-Class Multiple Classifier Systems for Secure Learning Against Evasion Attacks at Test Time

Biggio, B., Corona, I., He, Z.-M., Chan, P. P. K., Giacinto, G., Yeung, D. S., & Roli, F. (2015) · Lecture Notes in Computer Science

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 47

ALOHA: an architectural-aware framework for deep learning at the edge

Meloni, P., Loi, D., Deriu, G., Pimentel, A. D., Sapra, D., Moser, B., Shepeleva, N., Conti, F., Benini, L., Ripolles, O., et al. (2018) · Proceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications

Embedded Systems Design TechniquesAdvanced Neural Network Applications

Cited by 10