Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Granese, Federica"'
Autor:
Lence, Alex, Fall, Ahmad, Granese, Federica, Hanczar, Blaise, Salem, Joe-Elie, Zucker, Jean-Daniel, Prifti, Edi
In this work, we address the challenge of reconstructing the complete 12-lead ECG signal from incomplete parts of it. We focus on two main scenarii: (i) reconstructing missing signal segments within an ECG lead and (ii) recovering missing leads from
Externí odkaz:
http://arxiv.org/abs/2406.16901
This paper explores a scenario in which a malicious actor employs a multi-armed attack strategy to manipulate data samples, offering them various avenues to introduce noise into the dataset. Our central objective is to protect the data by detecting a
Externí odkaz:
http://arxiv.org/abs/2402.15808
Multi-armed adversarial attacks, in which multiple algorithms and objective loss functions are simultaneously used at evaluation time, have been shown to be highly successful in fooling state-of-the-art adversarial examples detectors while requiring
Externí odkaz:
http://arxiv.org/abs/2302.02216
We analyze to what extent final users can infer information about the level of protection of their data when the data obfuscation mechanism is a priori unknown to them (the so-called ''black-box'' scenario). In particular, we delve into the investiga
Externí odkaz:
http://arxiv.org/abs/2208.14414
Detection of adversarial examples has been a hot topic in the last years due to its importance for safely deploying machine learning algorithms in critical applications. However, the detection methods are generally validated by assuming a single impl
Externí odkaz:
http://arxiv.org/abs/2206.15415
Autor:
Granese, Federica, Romanelli, Marco, Gorla, Daniele, Palamidessi, Catuscia, Piantanida, Pablo
Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations where DNN are implemented as "black boxes". A promising approach to se
Externí odkaz:
http://arxiv.org/abs/2106.02395
Autor:
Granese, Federica1,2 (AUTHOR) federica.granese@inria.fr, Gorla, Daniele2 (AUTHOR), Palamidessi, Catuscia1 (AUTHOR)
Publikováno v:
International Journal of Information Security. Oct2021, Vol. 20 Issue 5, p763-782. 20p.