Lightweight MobileNet Model for Image Tempering Detection

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Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5.6524
Keywords:
Image Tampering Detection, MobileNet, CNN, CASIA v2, Deep Learning, Lightweight Architecture, Convolutional Neural Networks, Performance Evaluation

Main Article Content

Sajeeda Shikalgar
Research Scholar, Shri Venkateshwara University, Amroha, Uttar Pradesh, India
Rakesh K. Yadav
Director Academics, Shri Venkateshwara University, Amroha, Uttar Pradesh, India
Parikshit N. Mahalle
Professor and Head, Department of Artificial Intelligence and Data Science, Bansilal Ramnath Agarwal Charitable Trust's, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

Abstract

In recent years, there has been a wide range of image manipulation identification challenges and an overview of image tampering detection and the relevance of applying deep learning models such as CNN and MobileNet for this purpose. The discussion then delves into the construction and setup of these models, which includes a block diagram as well as mathematical calculations for each layer. A literature study on Image tampering detection is also included in the discussion, comparing and contrasting various articles and their methodologies. The study then moves on to training and assessment datasets, such as the CASIA v2 dataset, and performance indicators like as accuracy and loss. Lastly, the performance characteristics of the MobileNet and CNN designs are compared. This work focuses on Image tampering detection using convolutional neural networks (CNNs) and the MobileNet architecture. We reviewed the MobileNet architecture's setup and block diagram, as well as its application to Image tampering detection. We also looked at significant literature on Image manipulation detection, such as major studies and their methodologies. Using the CASIA v2 dataset, we evaluated the performance of MobileNet and CNN architectures in terms of accuracy and loss. This paper offered an overview of the usage of deep learning and CNN architectures for image tampering detection and proved their accuracy in detecting manipulated images.

Article Details

How to Cite
Shikalgar, S. ., Yadav, R. K. ., & Mahalle, P. N. . (2023). Lightweight MobileNet Model for Image Tempering Detection. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5), 55–69. https://doi.org/10.17762/ijritcc.v11i5.6524
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Vol. 11 No. 5 (2023): May (2023) Issue
Section
Articles

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