Zobrazeno 1 - 10
of 344
pro vyhledávání: '"Fabio Roli"'
Publikováno v:
IET Biometrics, Vol 11, Iss 1, Pp 63-78 (2022)
Abstract Electroencephalography (EEG)‐based personal recognition in realistic contexts is still a matter of research, with the following issues to be clarified: (1) the duration of the signal length, called ‘epoch’, which must be very short for
Externí odkaz:
https://doaj.org/article/f02f4cf6fdcd48dc8ddaffccb094540e
Autor:
Angelo Sotgiu, Ambra Demontis, Marco Melis, Battista Biggio, Giorgio Fumera, Xiaoyi Feng, Fabio Roli
Publikováno v:
EURASIP Journal on Information Security, Vol 2020, Iss 1, Pp 1-10 (2020)
Abstract Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples that are carefully perturbed to cause misclassification at tes
Externí odkaz:
https://doaj.org/article/26aa91538cec41a1898b25e1e12d9db9
Autor:
Sara Concas, Simone Maurizio La Cava, Giulia Orrù, Carlo Cuccu, Jie Gao, Xiaoyi Feng, Gian Luca Marcialis, Fabio Roli
Publikováno v:
Applied Sciences, Vol 12, Iss 15, p 7365 (2022)
Deepfake detection is of fundamental importance to preserve the reliability of multimedia communications. Modern deepfake detection systems are often specialized on one or more types of manipulation but are not able to generalize. On the other hand,
Externí odkaz:
https://doaj.org/article/dc5e2455ebda4ec999290c42b663d35c
Autor:
Bahram Lavi Giorgio Fumera, Fabio Roli
Publikováno v:
IET Computer Vision, Vol 12, Iss 4, Pp 513-519 (2018)
One of the goals of person re‐identification systems is to support video‐surveillance operators and forensic investigators to find an individual of interest in videos acquired by a network of non‐overlapping cameras. This is attained by sorting
Externí odkaz:
https://doaj.org/article/5356a492e33a439cbc2a4408079c8796
Autor:
Yang Zheng, Xiaoyi Feng, Zhaoqiang Xia, Xiaoyue Jiang, Ambra Demontis, Maura Pintor, Battista Biggio, Fabio Roli
Publikováno v:
Information Sciences. 632:130-143
Adversarial reprogramming allows repurposing a machine-learning model to perform a different task. For example, a model trained to recognize animals can be reprogrammed to recognize digits by embedding an adversarial program in the digit images provi
Publikováno v:
IEEE Internet of Things Journal. 10:2646-2657
A Hybrid Training-Time and Run-Time Defense Against Adversarial Attacks in Modulation Classification
Publikováno v:
IEEE Wireless Communications Letters. 11:1161-1165
Publikováno v:
IET Biometrics, Vol 11, Iss 1, Pp 63-78 (2022)
Electroencephalography (EEG)‐based personal recognition in realistic contexts is still a matter of research, with the following issues to be clarified: (1) the duration of the signal length, called ‘epoch’, which must be very short for practica
Autor:
Xinglong Chang, Katharina Dost, Kaiqi Zhao, Ambra Demontis, Fabio Roli, Gillian Dobbie, Jörg Wicker
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783031333736
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::528e2fe24ce72666f6ab0a406a9ae67d
https://doi.org/10.1007/978-3-031-33374-3_1
https://doi.org/10.1007/978-3-031-33374-3_1
Publikováno v:
IEEE Wireless Communications Letters. 10:1830-1834
Deep learning algorithms have been shown to be powerful in many communication network design problems, including that in automatic modulation classification. However, they are vulnerable to carefully crafted attacks called adversarial examples. Hence