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pro vyhledávání: '"Angioni, Daniele"'
Autor:
Angioni, Daniele, Demetrio, Luca, Pintor, Maura, Oneto, Luca, Anguita, Davide, Biggio, Battista, Roli, Fabio
Machine-learning models demand for periodic updates to improve their average accuracy, exploiting novel architectures and additional data. However, a newly-updated model may commit mistakes that the previous model did not make. Such misclassification
Externí odkaz:
http://arxiv.org/abs/2402.17390
Autor:
Ledda, Emanuele, Angioni, Daniele, Piras, Giorgio, Fumera, Giorgio, Biggio, Battista, Roli, Fabio
Machine-learning models can be fooled by adversarial examples, i.e., carefully-crafted input perturbations that force models to output wrong predictions. While uncertainty quantification has been recently proposed to detect adversarial inputs, under
Externí odkaz:
http://arxiv.org/abs/2309.10586
The presence and persistence of Android malware is an on-going threat that plagues this information era, and machine learning technologies are now extensively used to deploy more effective detectors that can block the majority of these malicious prog
Externí odkaz:
http://arxiv.org/abs/2208.04838
Autor:
Pintor, Maura, Angioni, Daniele, Sotgiu, Angelo, Demetrio, Luca, Demontis, Ambra, Biggio, Battista, Roli, Fabio
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding, and requires careful hyperparameter tuning, potentially leadi
Externí odkaz:
http://arxiv.org/abs/2203.04412
Autor:
Casula, Roberto, Orrù, Giulia, Angioni, Daniele, Feng, Xiaoyi, Marcialis, Gian Luca, Roli, Fabio
We investigated the threat level of realistic attacks using latent fingerprints against sensors equipped with state-of-art liveness detectors and fingerprint verification systems which integrate such liveness algorithms. To the best of our knowledge,
Externí odkaz:
http://arxiv.org/abs/2007.03397
Autor:
Pintor, Maura, Angioni, Daniele, Sotgiu, Angelo, Demetrio, Luca, Demontis, Ambra, Biggio, Battista, Roli, Fabio
Publikováno v:
In Pattern Recognition February 2023 134