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

ASIA unversity > 資訊學院 > 資訊工程學系 > 期刊論文 > Item 310904400/115524

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://asiair.asia.edu.tw/ir/handle/310904400/115524

题名:	AI-enabled digital forgery analysis and crucial interactions monitoring in smart communities
作者:	Sedik, Ahmed;Sedik, Ahmed;Male, Yassine;Maleh, Yassine;El, Ghada M.;Banby, Ghada M. El;Ashraf, Ashraf A.M.;Khalaf, Ashraf A.M.;Abd, Fathi E.;Fathi, E. Abd El-Samie;Bhoosha, Brij;Gupta, Brij Bhooshan;Konstantinos;Psannis, Konstantinos;Abd, Ahmed A.;Ahmed, Ahmed A. Abd El-Latif
贡献者:	資訊電機學院資訊工程學系
关键词:	Forgery detection；Deep learning；IoT；Smart cities；Security analysis
日期:	2022-04-01
上传时间:	2023-03-29 02:30:30 (UTC+0)
出版者:	亞洲大學
摘要:	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.
显示于类别:	[資訊工程學系] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
index.html		0Kb	HTML	101	检视/开启

在ASIAIR中所有的数据项都受到原著作权保护.

数据加载中.....