Ambra Demontis
Ambra Demontis
Assistant Professor at University of Cagliari
Verified email at unica.it - Homepage
Title
Cited by
Cited by
Year
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
2082017
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
1362017
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
1152018
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
722019
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
482017
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
482016
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
232016
Adversarial detection of flash malware: Limitations and open issues
D Maiorca, A Demontis, B Biggio, F Roli, G Giacinto
Computers & Security 96, 101901, 2020
82020
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
82018
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
82017
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
72019
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
62017
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
42020
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
32020
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
32016
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
32015
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, ...
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Articles 1–20