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 |
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