| Towards poisoning of deep learning algorithms with back-gradient optimization L Muñoz-González, B Biggio, A Demontis, A Paudice, V Wongrassamee, ... Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security …, 2017 | 208 | 2017 |
| Yes, machine learning can be more secure! a case study on android malware detection A Demontis, M Melis, B Biggio, D Maiorca, D Arp, K Rieck, I Corona, ... IEEE Transactions on Dependable and Secure Computing, 2017 | 136 | 2017 |
| Adversarial malware binaries: Evading deep learning for malware detection in executables B Kolosnjaji, A Demontis, B Biggio, D Maiorca, G Giacinto, C Eckert, ... 2018 26th European signal processing conference (EUSIPCO), 533-537, 2018 | 115 | 2018 |
| Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks A Demontis, M Melis, M Pintor, M Jagielski, B Biggio, A Oprea, ... 28th {USENIX} Security Symposium ({USENIX} Security 19), 321-338, 2019 | 72 | 2019 |
| Is deep learning safe for robot vision? adversarial examples against the icub humanoid M Melis, A Demontis, B Biggio, G Brown, G Fumera, F Roli Proceedings of the IEEE International Conference on Computer Vision …, 2017 | 48 | 2017 |
| Secure kernel machines against evasion attacks P Russu, A Demontis, B Biggio, G Fumera, F Roli Proceedings of the 2016 ACM workshop on artificial intelligence and security …, 2016 | 48 | 2016 |
| On security and sparsity of linear classifiers for adversarial settings A Demontis, P Russu, B Biggio, G Fumera, F Roli Joint IAPR International Workshops on Statistical Techniques in Pattern …, 2016 | 23 | 2016 |
| Adversarial detection of flash malware: Limitations and open issues D Maiorca, A Demontis, B Biggio, F Roli, G Giacinto Computers & Security 96, 101901, 2020 | 8 | 2020 |
| On the intriguing connections of regularization, input gradients and transferability of evasion and poisoning attacks A Demontis, M Melis, M Pintor, M Jagielski, B Biggio, A Oprea, ... arXiv preprint arXiv:1809.02861, 2018 | 8 | 2018 |
| Infinity-norm support vector machines against adversarial label contamination A Demontis, B Biggio, G Fumera, G Giacinto, F Roli 1st Italian Conference on Cybersecurity, ITASEC 2017 1816, 106-115, 2017 | 8 | 2017 |
| secml: A python library for secure and explainable machine learning M Melis, A Demontis, M Pintor, A Sotgiu, B Biggio arXiv preprint arXiv:1912.10013, 2019 | 7 | 2019 |
| Is deep learning safe for robot vision M Melis, A Demontis, B Biggio, G Brown, G Fumera, F Roli Adversarial examples against the iCub humanoid. CoRR, abs/1708.06939, 2017 | 6 | 2017 |
| Deep neural rejection against adversarial examples A Sotgiu, A Demontis, M Melis, B Biggio, G Fumera, X Feng, F Roli EURASIP Journal on Information Security 2020, 1-10, 2020 | 4 | 2020 |
| Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware? M Melis, M Scalas, A Demontis, D Maiorca, B Biggio, G Giacinto, F Roli arXiv preprint arXiv:2005.01452, 2020 | 3 | 2020 |
| Super-sparse learning in similarity spaces A Demontis, M Melis, B Biggio, G Fumera, F Roli IEEE Computational Intelligence Magazine 11 (4), 36-45, 2016 | 3 | 2016 |
| Super-sparse regression for fast age estimation from faces at test time A Demontis, B Biggio, G Fumera, F Roli International Conference on Image Analysis and Processing, 551-562, 2015 | 3 | 2015 |
| AISec'20: 13th Workshop on Artificial Intelligence and Security S Afroz, N Carlini, A Demontis Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications …, 2020 | | 2020 |
| Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers? S Melacci, G Ciravegna, A Sotgiu, A Demontis, B Biggio, M Gori, F Roli arXiv preprint arXiv:2006.03833, 2020 | | 2020 |
| Securing Machine Learning against Adversarial Attacks A Demontis, F Roli, B Biggio | | 2018 |
| Workshop Chair B Biggio, A Demontis, K Rieck, D Evans, C Wressnegger, PT FireEye, ... | | |
Battista BiggioAssistant Professor at University of Cagliari, Co-Founder of Pluribus OneVerified email at diee.unica.it
