Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Afifatul Mukaroh"'
Autor:
Yongsu Kim, Hyoeun Kang, Naufal Suryanto, Harashta Tatimma Larasati, Afifatul Mukaroh, Howon Kim
Publikováno v:
Sensors, Vol 21, Iss 16, p 5323 (2021)
Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world at
Externí odkaz:
https://doaj.org/article/78cdddc6fb5842c19ab4f2bc3ba9f815
Publikováno v:
Sensors, Vol 20, Iss 19, p 5674 (2020)
Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems
Externí odkaz:
https://doaj.org/article/d1e3e83a4fc74cfc846d34aa3a361d2c
Autor:
Afifatul Mukaroh, Priyo Sidik Sasongko
Publikováno v:
JURNAL MASYARAKAT INFORMATIKA. 11:27-35
Dalam pengawasan lalu lintas, pengenalan nomor pelat kendaraan menjadi penting untuk dilakukan. Hal ini dikarenakan pengenalan nomor pelat kendaraan memiliki banyak tujuan seperti identifikasi kendaraan curian, identifikasi kendaraan yang melanggar t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d73c0a965af4619c791283e3e06963b4
https://doi.org/10.14710/jmasif.11.2.34870
https://doi.org/10.14710/jmasif.11.2.34870
Autor:
Harashta Tatimma Larasati, Hyoeun Kang, Howon Kim, Afifatul Mukaroh, Naufal Suryanto, Yongsu Kim
Publikováno v:
Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 16
Sensors, Vol 21, Iss 5323, p 5323 (2021)
Sensors
Volume 21
Issue 16
Sensors, Vol 21, Iss 5323, p 5323 (2021)
Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world at
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3620f7c9afb92b557ac53813630a2f4
https://pubmed.ncbi.nlm.nih.gov/34450763
https://pubmed.ncbi.nlm.nih.gov/34450763
Publikováno v:
Sensors (Basel, Switzerland)
Sensors
Volume 20
Issue 19
Sensors, Vol 20, Iss 5674, p 5674 (2020)
Sensors
Volume 20
Issue 19
Sensors, Vol 20, Iss 5674, p 5674 (2020)
Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0809ddc64a0a2f9a8b5ce59c17cf4fa4
https://pubmed.ncbi.nlm.nih.gov/33027898
https://pubmed.ncbi.nlm.nih.gov/33027898
Autor:
Harashta Tatimma Larasati, Afifatul Mukaroh, Howon Kim, Yongsu Kim, Naufal Suryanto, Hyoeun Kang
Publikováno v:
Information Security Applications ISBN: 9783030652982
WISA
WISA
Deep Neural Networks (DNNs) are very vulnerable to adversarial attacks because of the instability and unreliability under the training process. Recently, many studies about adversarial patches have been conducted that aims to misclassify the image cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc17ae47a1e3ebcf2a8a23d72f236dd8
https://doi.org/10.1007/978-3-030-65299-9_1
https://doi.org/10.1007/978-3-030-65299-9_1
