Ambra Demontis
Ambra Demontis
Assistant Professor at University of Cagliari
Email verificata su - Home page
Citata da
Citata da
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
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
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
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
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
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
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
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
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
Adversarial detection of flash malware: Limitations and open issues
D Maiorca, A Demontis, B Biggio, F Roli, G Giacinto
Computers & Security 96, 101901, 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
International journal of machine learning and cybernetics, 1-16, 2022
Can domain knowledge alleviate adversarial attacks in multi-label classifiers?
S Melacci, G Ciravegna, A Sotgiu, A Demontis, B Biggio, M Gori, F Roli
The threat of offensive ai to organizations
Y Mirsky, A Demontis, J Kotak, R Shankar, D Gelei, L Yang, X Zhang, ...
Computers & Security, 103006, 2022
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, ...
arXiv preprint arXiv:2205.01992, 2022
Infinity-norm support vector machines against adversarial label contamination
A Demontis, B Biggio, G Fumera, G Giacinto, F Roli
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
Super-sparse regression for fast age estimation from faces at test time
A Demontis, B Biggio, G Fumera, F Roli
Image Analysis and Processing—ICIAP 2015: 18th International Conference …, 2015
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, 2023
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
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
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20