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

Author

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

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.

Suggested Citation

  • Seddik, Ahmed & Maleh, Yassine & El Banby, Ghada M. & Khalaf, Ashraf A.M. & Abd El-Samie, Fathi E. & Gupta, Brij B & Psannis, Konstantinos & Abd El-Latif, Ahmed A., 2022. "AI-enabled digital forgery analysis and crucial interactions monitoring in smart communities," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:tefoso:v:177:y:2022:i:c:s0040162522000877
    DOI: 10.1016/j.techfore.2022.121555
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    References listed on IDEAS

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