An improvised CNN model for fake image detection

被引：24

作者：

Hamid Y. ^{[1
]}

Elyassami S. ^{[1
]}

Gulzar Y. ^{[2
]}

Balasaraswathi V.R. ^{[3
]}

Habuza T. ^{[4
]}

Wani S. ^{[5
]}

机构：

[1] Information Security and Engineering Technology, Abu Dhabi Polytechnic, Abu Dhabi

[2] Department of Management Information System, College of Business Administration, King Faisal University, Riyadh

[3] Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Chengalpattu

[4] Department of Computer Science and SWE, College of Info. Tech, UAEU, Al Ain

[5] Department of Computer Science, International Islamic University Malaysia, Kuala Lumpur

来源：

International Journal of Information Technology | 2023年 / 15卷 / 1期

关键词：

Artificial Intelligence; Classification; Convolution neural networks; Doctored images; Splicing;

D O I：

10.1007/s41870-022-01130-5

中图分类号：

学科分类号：

摘要：

The last decade has witnessed a multifold growth of image data courtesy of the emergence of social networking services like Facebook, Instagram, LinkedIn etc. The major menace faced by today’s world is the issue of doctored images, where-in the photographs are altered using a rich set of ways like splicing, copy-move, removal to change their meaning and hence demands serious mitigation mechanisms to be thought of. The problem when seen from the prism of Artificial intelligence is a binary classification one, where-in the characterization must be drawn between the original and the manipulated images. This research work proposes a computer vision model based on Convolution Neural Networks for fake image detection. A comparative analysis of 6 popular traditional machine learning models and 6 different CNN architectures to select the best possible model for further experimentation. The proposed model based on ResNet50 employed with powerful preprocessing techniques results in a perfect fake image detector having a total accuracy of 0.99 having an improvement of around 18% performance with other models. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

引用

页码：5 / 15

页数：10

共 43 条

[1]

Obermayer N., Kovari E., Leinonen J., Bak G., Valeri M., How social media practices shape family business performance: the wine industry case study, Eur Manag J, 40, 3, pp. 360-371, (2022)

[2]

Kasra M., Shen C., O'brien J.F., Seeing is believing: How people fail to identify fake images on the web, Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, (2018)

[3]

Allcott H., Gentzkow M., Social media and fake news in the 2016 election, J Econ Perspect, 31, 2, pp. 211-236, (2017)

[4]

Elyassami S., Et al., Fake news detection using ensemble learning and machine learning algorithms, Combating Fake News with Computational Intelligence Techniques, pp. 149-162, (2022)

[5]

Elyassami S., Albloushi S., Alnuaimi M.A., Alhosani O., Ali H.A., Almarashda K., Intelligent models for mining social media data, Advances on Smart and Soft Computing, pp. 199-207, (2022)

[6]

Elyassami S., Nasir Humaid H., Ali Alhosani A., Alawadhi H.T., Artificial Intelligence-Based Digital Financial Fraud Detection, International Conference on Intelligent and Fuzzy Systems, pp. 214-221, (2021)

[7]

Al-Karaki J.N., Ababneh N., Hamid Y., Gawanmeh A., Evaluating the effectiveness of distance learning in higher education during COVID-19 global crisis: UAE educators’ perspectives, Contemp Educ Technol, 13, 3, (2021)

[8]

Zhai X., Chu X., Chai C.S., Jong M.S., Istenic A., Spector M., Liu J.B., Yuan J., Li Y., A review of artificial intelligence (AI) in education from 2010 to 2020, Complexity, (2021)

[9]

Yasir H., Et al., A Simple and Predictive Model for COVID-19 Evolution in Large Scale Infected Countries, (2020)

[10]

Khan S.A., Gulzar Y., Turaev S., Peng Y.S., A modified HSIFT Descriptor for medical image classification of anatomy objects, Symmetry, 13, 11, (1987)

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