Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis Z Chen, L Demetrio, S Gupta, X Feng, Z Xia, AE Cinà, M Pintor, L Oneto, ... arXiv preprint arXiv:2406.10090, 2024 | | 2024 |
AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples AE Cinà, J Rony, M Pintor, L Demetrio, A Demontis, B Biggio, IB Ayed, ... arXiv preprint arXiv:2404.19460, 2024 | 1 | 2024 |
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates D Angioni, L Demetrio, M Pintor, L Oneto, D Anguita, B Biggio, F Roli arXiv preprint arXiv:2402.17390, 2024 | | 2024 |
-zero: Gradient-based Optimization of -norm Adversarial Examples AE Cinà, F Villani, M Pintor, L Schönherr, B Biggio, M Pelillo arXiv preprint arXiv:2402.01879, 2024 | | 2024 |
Rethinking data augmentation for adversarial robustness H Eghbal-zadeh, W Zellinger, M Pintor, K Grosse, K Koutini, BA Moser, ... Information Sciences 654, 119838, 2024 | 2 | 2024 |
Chairs Introduction and Welcome to AISec 2023 M Pintor, X Chen, F Tramèr AISec 2023-Proceedings of the 16th ACM Workshop on Artificial Intelligence …, 2023 | | 2023 |
Raze to the ground: Query-efficient adversarial html attacks on machine-learning phishing webpage detectors B Montaruli, L Demetrio, M Pintor, L Compagna, D Balzarotti, B Biggio Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security …, 2023 | 3 | 2023 |
AISec'23: 16th ACM Workshop on Artificial Intelligence and Security M Pintor, FS Tramèr, X Chen Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications …, 2023 | | 2023 |
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization G Floris, R Mura, L Scionis, G Piras, M Pintor, A Demontis, B Biggio arXiv preprint arXiv:2310.08177, 2023 | | 2023 |
Minimizing energy consumption of deep learning models by energy-aware training D Lazzaro, AE Cinà, M Pintor, A Demontis, B Biggio, F Roli, M Pelillo International Conference on Image Analysis and Processing, 515-526, 2023 | 6 | 2023 |
Stateful detection of adversarial reprogramming Y Zheng, X Feng, Z Xia, X Jiang, M Pintor, A Demontis, B Biggio, F Roli Information Sciences 642, 119093, 2023 | 2 | 2023 |
Detecting Attacks Against Deep Reinforcement Learning for Autonomous Driving M Pintor, L Demetrio, A Sotgiu, HY Lin, C Fang, A Demontis, B Biggio 2023 International Conference on Machine Learning and Cybernetics (ICMLC), 57-62, 2023 | | 2023 |
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks G Piras, M Pintor, A Demontis, B Biggio 2023 International Conference on Machine Learning and Cybernetics (ICMLC …, 2023 | | 2023 |
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 | 20 | 2023 |
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 | 41 | 2023 |
Cybersecurity and AI: The PRALab Research Experience M Pintor, G Orrú, D Maiorca, A Demontis, L Demetrio, G Marcialis, ... CEUR WORKSHOP PROCEEDINGS 2486, 426-431, 2023 | | 2023 |
AI Security and Safety: The PRALab Research Experience A Demontis, M Pintor, L Demetrio, A Sotgiu, D Angioni, G Piras, S Gupta, ... CEUR WORKSHOP PROCEEDINGS 3486, 324-328, 2023 | | 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 | 76 | 2023 |
A survey on reinforcement learning security with application to autonomous driving A Demontis, M Pintor, L Demetrio, K Grosse, HY Lin, C Fang, B Biggio, ... arXiv preprint arXiv:2212.06123, 2022 | 3 | 2022 |
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 | 33 | 2022 |