Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο:
https://ruomo.lib.uom.gr/handle/7000/1834
Πλήρης εγγραφή μεταδεδομένων
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Sedik, Ahmed | - |
dc.contributor.author | Maleh, Yassine | - |
dc.contributor.author | El Banby, Ghada M. | - |
dc.contributor.author | Khalaf, Ashraf A.M. | - |
dc.contributor.author | Abd El-Samie, Fathi E. | - |
dc.contributor.author | Gupta, Brij B. | - |
dc.contributor.author | Psannis, Kostas E. | - |
dc.contributor.author | Abd El-Latif, Ahmed A. | - |
dc.date.accessioned | 2023-12-04T19:11:11Z | - |
dc.date.available | 2023-12-04T19:11:11Z | - |
dc.date.issued | 2022-04 | - |
dc.identifier | 10.1016/j.techfore.2022.121555 | en_US |
dc.identifier.issn | 0040-1625 | en_US |
dc.identifier.uri | https://doi.org/10.1016/j.techfore.2022.121555 | en_US |
dc.identifier.uri | https://ruomo.lib.uom.gr/handle/7000/1834 | - |
dc.description.abstract | Digital forgery has become one of the attractive research fields in today’s technology. There are several types of forgery in digital media transmission, especially digital image transmission. A common type of forgery is copy-move forgery (CMF). The CMF may be encountered in streets, railway stations, underground stations, or festivals. This type of forgery may lead to hugger-mugger in some cases. Therefore, there is a need to find a sufficient countermeasure mechanism to detect image forgeries. This paper presents a new CMFD approach that depends on deep learning for IoT based smart cities. Two well-known deep learning models, namely CNN and ConvLSTM, are adopted for CMFD. The proposed models are tested on MICC-220, MICC-600 and MICC 2000 datasets for validation. Several tests are performed to verify the effectiveness of the proposed models. The simulation results reveal that the testing accuracy reaches 95%, 73%, and 94% for MICC-F220, MICC-F600 and MICC-F2000 datasets. In addition, the proposed approach achieves an accuracy of 85% for a combined set of all datasets. | en_US |
dc.language.iso | en | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source | Technological Forecasting and Social Change | en_US |
dc.subject | FRASCATI::Engineering and technology | en_US |
dc.subject.other | Forgery detection | en_US |
dc.subject.other | Deep learning | en_US |
dc.subject.other | IoT | en_US |
dc.subject.other | Smart cities | en_US |
dc.subject.other | Security analysis | en_US |
dc.title | AI-enabled digital forgery analysis and crucial interactions monitoring in smart communities | en_US |
dc.type | Article | en_US |
dc.contributor.department | Τμήμα Εφαρμοσμένης Πληροφορικής | en_US |
local.identifier.volume | 177 | en_US |
local.identifier.firstpage | 121555 | en_US |
Εμφανίζεται στις Συλλογές: | Τμήμα Εφαρμοσμένης Πληροφορικής |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
---|---|---|---|---|
AI-enabled digital forgery analysis and crucial interactions monitoring in smart com.pdf | AI-enabled digital forgery analysis and crucial interactions monitoring in smart com.pdf | 905,54 kB | Adobe PDF | Προβολή/Ανοιγμα |
Αυτό το τεκμήριο προστατεύεται από Αδεια Creative Commons