| 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 | 740 | 2017 |
| 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 | 487 | 2019 |
| 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 | 447 | 2018 |
| 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 | 363 | 2017 |
| 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 | 127 | 2017 |
| Wild patterns reloaded: A survey of machine learning security against training data poisoning AE Cinà, K Grosse, A Demontis, S Vascon, W Zellinger, BA Moser, ... ACM Computing Surveys 55 (13s), 1-39, 2023 | 114 | 2023 |
| The threat of offensive ai to organizations Y Mirsky, A Demontis, J Kotak, R Shankar, D Gelei, L Yang, X Zhang, ... Computers & Security 124, 103006, 2023 | 93 | 2023 |
| 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 | 79 | 2016 |
| 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 | 71 | 2020 |
| secml: Secure and explainable machine learning in Python M Pintor, L Demetrio, A Sotgiu, M Melis, A Demontis, B Biggio SoftwareX 18, 101095, 2022 | 63* | 2022 |
| ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches M Pintor, D Angioni, A Sotgiu, L Demetrio, A Demontis, B Biggio, F Roli Pattern Recognition 134, 109064, 2023 | 50 | 2023 |
| On security and sparsity of linear classifiers for adversarial settings A Demontis, P Russu, B Biggio, G Fumera, F Roli Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2016 | 46 | 2016 |
| Machine learning security against data poisoning: Are we there yet? AE Cinà, K Grosse, A Demontis, B Biggio, F Roli, M Pelillo Computer 57 (3), 26-34, 2024 | 39 | 2024 |
| Indicators of attack failure: Debugging and improving optimization of adversarial examples M Pintor, L Demetrio, A Sotgiu, A Demontis, N Carlini, B Biggio, F Roli Advances in Neural Information Processing Systems 35, 23063-23076, 2022 | 38 | 2022 |
| 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 International journal of machine learning and cybernetics, 1-16, 2022 | 37 | 2022 |
| Adversarial detection of flash malware: Limitations and open issues D Maiorca, A Demontis, B Biggio, F Roli, G Giacinto Computers & Security 96, 101901, 2020 | 34 | 2020 |
| Domain knowledge alleviates adversarial attacks in multi-label classifiers S Melacci, G Ciravegna, A Sotgiu, A Demontis, B Biggio, M Gori, F Roli IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (12), 9944 …, 2021 | 33* | 2021 |
| Energy-latency attacks via sponge poisoning AE Cinà, A Demontis, B Biggio, F Roli, M Pelillo arXiv preprint arXiv:2203.08147, 2022 | 22 | 2022 |
| Why adversarial reprogramming works, when it fails, and how to tell the difference Y Zheng, X Feng, Z Xia, X Jiang, A Demontis, M Pintor, B Biggio, F Roli Information Sciences 632, 130-143, 2023 | 21 | 2023 |
| The hammer and the nut: Is bilevel optimization really needed to poison linear classifiers? AE Cinà, S Vascon, A Demontis, B Biggio, F Roli, M Pelillo 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 18 | 2021 |
Battista BiggioFull Professor, University of Cagliari, ItalyVerified email at unica.it
