AI-enabled digital forgery analysis and crucial interactions monitoring in smart communities

Ahmed Seddik, Yassine Maleh, Ghada M. El Banby, Ashraf A.M. Khalaf, Fathi E. Abd El-Samie, Brij B Gupta, Konstantinos Psannis and Ahmed A. Abd El-Latif

Technological Forecasting and Social Change, 2022, vol. 177, issue C

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.

Keywords: Forgery detection; Deep learning; IoT; Smart cities; Security analysis (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:177:y:2022:i:c:s0040162522000877

DOI: 10.1016/j.techfore.2022.121555

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