Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1834
Title: AI-enabled digital forgery analysis and crucial interactions monitoring in smart communities
Authors: Sedik, Ahmed
Maleh, Yassine
El Banby, Ghada M.
Khalaf, Ashraf A.M.
Abd El-Samie, Fathi E.
Gupta, Brij B.
Psannis, Kostas E.
Abd El-Latif, Ahmed A.
Type: Article
Subjects: FRASCATI::Engineering and technology
Keywords: Forgery detection
Deep learning
IoT
Smart cities
Security analysis
Issue Date: Apr-2022
Source: Technological Forecasting and Social Change
Volume: 177
First Page: 121555
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.
URI: https://doi.org/10.1016/j.techfore.2022.121555
https://ruomo.lib.uom.gr/handle/7000/1834
ISSN: 0040-1625
Other Identifiers: 10.1016/j.techfore.2022.121555
Appears in Collections:Department of Applied Informatics

Files in This Item:
File Description SizeFormat 
AI-enabled digital forgery analysis and crucial interactions monitoring in smart com.pdf
  Until 2024-04-01
AI-enabled digital forgery analysis and crucial interactions monitoring in smart com.pdf905,54 kBAdobe PDFView/Open Request a copy


This item is licensed under a Creative Commons License Creative Commons