An Optimized Deep Features for Detecting Tampered Region from the Copy Move Forgery Image
An Optimized Deep Features for Detecting Tampered Region from the Copy Move Forgery Image |
||
 |
 |
|
| © 2024 by IJETT Journal | ||
| Volume-72 Issue-7 |
||
| Year of Publication : 2024 | ||
| Author : Allu Venkateswara Rao, D. Madhavi |
||
| DOI : 10.14445/22315381/IJETT-V72I7P124 | ||
How to Cite?
Allu Venkateswara Rao, D. Madhavi, "An Optimized Deep Features for Detecting Tampered Region from the Copy Move Forgery Image," International Journal of Engineering Trends and Technology, vol. 72, no. 7, pp. 224-236, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I7P124
Abstract
Nowadays, forgery detection systems have rapidly grown in the digital application to find crime events. However, detecting the forgery and identifying the forged tampered portion is more complex because of the noisy data. To overcome this issue, the current research article has aimed to develop a novel Lion-based Optimized Radial Basis Neural Model (LORBNM). Initially, the CoMoFoD dataset has been trained, the training noise has been removed from the pre-processing layer, and then the error-free images are entered into the classification layer. Consequently, the classification parameters were tuned, and the present features were extracted. Furthermore, the image types have been specified in terms of Computer-Generated-Image (CGI), Natural Image (NI), and Forgery Image (FI). Eventually, the tapered region was predicted and segmented from the forgery image, and then the key metrics were calculated and compared with other existing approaches. In that, the presented LORBNM has observed the finest segmentation exactness score.
Keywords
Forgery Detection, Neural Networks, Copy-Move-Forgery-Image, Image Type Classification, Tampered Region Prediction.
References
[1] Muhammad Bilal et al., “A Robust Technique for Copy-Move Forgery Detection from Small and Extremely Smooth Tampered Regions Based on the DHE-SURF Features and mDBSCAN Clustering,” Australian Journal of Forensic Sciences, vol. 53, no. 4, pp. 459-482, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] X.H. Zhou, and Q.J. Shi, “Multiple Copy-Move Forgery Detection Based on Density Clustering,” Pattern Recognition and Image Analysis, vol. 31, pp. 109-116, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] P. Niu et al., “Fast and Effective Keypoint-Based Image Copy-Move Forgery Detection Using Complex-Valued Moment Invariants,” Journal of Visual Communication and Image Representation, vol. 77, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Ankit Kumar Jaiswal, and Rajeev Srivastava, “Detection of Copy-Move Forgery in Digital Image Using Multi-Scale, Multi-Stage Deep Learning Model,” Neural Processing Letters, vol. 54, no. 1, pp. 75-100, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Ritu Agarwal, and Om Prakash Verma, “Robust Copy-Move Forgery Detection Using Modified Superpixel Based FCM Clustering with Emperor Penguin Optimization and Block Feature Matching,” Evolving Systems, vol. 13, no. 1, pp. 27-41, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Gul Tahaoglu et al., “Improved Copy Move Forgery Detection Method via L*a*b* Color Space and Enhanced Localization Technique,” Multimedia Tools and Applications, vol. 80, pp. 23419-23456, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Anuja Dixit, and Soumen Bag, “A Fast Technique to Detect Copy-Move Image Forgery with Reflection and Non-Affine Transformation Attacks,” Expert Systems with Applications, vol.182, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Esteban Alejandro Armas Vega et al., “Copy-Move Forgery Detection Technique Based on Discrete Cosine Transform Blocks Features,” Neural Computing and Applications, vol. 33, pp. 4713-4727, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Kavita Rathi, and Parvinder Singh, “Copy–Move Forgery Detection by Using Key-Point-Based Harris Features and CLA Clustering,” New Approaches for Multidimensional Signal Processing: Proceedings of International Workshop, pp. 113-124, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Preeti Kale, Vijashree. A. More, and Ulhas Shinde, “Copy Move Forgery Detection-A Robust Technique,” 2021 Sixth International Conference on Image Information Processing, Shimla, India, pp. 121-125, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Kha-Tu Huynh, Tu-Nga Ly, and Phuong-Thanh Nguyen, “Improving the Accuracy in Copy-Move Image Detection: A Model of Sharpness and Blurriness,” SN Computer Science, vol. 2, no. 4, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] D. Suganya, K. Thirunadana Sikamani, and J. Sasikala, “Copy-Move Forgery Detection of Medical Images Using Most Valuable Player Based Optimization,” Sensing and Imaging, vol. 22, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Gaël Mahfoudi et al., “CMID: A New Dataset for Copy-Move Forgeries on ID Documents,” 2021 IEEE International Conference on Image Processing, Anchorage, AK, USA, pp. 3028-3032, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Nidhi Goel, Samarjeet Kaur, and Ruchika Bala, “Dual Branch Convolutional Neural Network for Copy Move Forgery Detection,” IET Image Processing, vol. 15, no. 3, pp. 656-665, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Anuj Rani, Ajit Jain, and Manoj Kumar, “Identification of Copy-Move and Splicing Based Forgeries Using Advanced SURF and Revised Template Matching,” Multimedia Tools and Applications, vol. 80, no. 16, pp. 23877-23898, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Sreenivasu Tinnathi, and G. Sudhavani, “An Efficient Copy Move Forgery Detection Using Adaptive Watershed Segmentation with AGSO and Hybrid Feature Extraction,” Journal of Visual Communication and Image Representation, vol. 74, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Mohassin Ahmad, and Farida Khursheed, “A Novel Image Tamper Detection Approach by Blending Forensic Tools and Optimized CNN: Sealion Customized Firefly Algorithm,” Multimedia Tools and Applications, vol. 81, no. 2, pp. 2577-2601, 2022.
[CrossRef] [Googkle Scholar] [Publisher Link]
[18] Jixiang Yang et al., “A Novel Copy-Move Forgery Detection Algorithm via Two-Stage Filtering,” Digital Signal Processing, vol. 113, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Qiyue Lyu et al., “Copy Move Forgery Detection Based on Double Matching,” Journal of Visual Communication and Image Representation, vol. 76, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Yilan Wang, Xiaobing Kang, and Yajun Chen, “Robust and Accurate Detection of Image Copy-Move Forgery Using PCET-SVD and Histogram of Block Similarity Measures,” Journal of Information Security and Applications, vol. 54, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Ritu Agarwal, and Om Prakash Verma, “An Efficient Copy Move Forgery Detection Using Deep Learning Feature Extraction and Matching Algorithm,” Multimedia Tools and Applications, vol. 79, no. 11, pp. 7355-7376, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Parag C. Pendharkar, “Hybrid Radial Basis Function DEA and its Applications to Regression, Segmentation and Cluster Analysis Problems,” Machine Learning with Applications, vol. 6, pp. 1-9, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[23] G. Saranraj, K. Selvamani, and P. Malathi, “A Novel Data Aggregation Using Multi Objective Based Male Lion Optimization Algorithm (DA-MOMLOA) in Wireless Sensor Network,” Journal of Ambient Intelligence and Humanized Computing, vol. 13, pp. 5645-5653, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] B. Chaitra, and P.V. Bhaskar Reddy, “An Approach for Copy-Move Image Multiple Forgery Detection Based on an Optimized Pre-Trained Deep Learning Model,” Knowledge-Based Systems, vol. 269, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Xiang-yang Wang et al., “Accurate and Robust Image Copy-Move Forgery Detection Using Adaptive Keypoints and FQGPCET-GLCM Feature,” Multimedia Tools and Applications, vol. 83, no. 1, pp. 2203-2235, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Navneet Kaur, Neeru Jindal, and Kulbir Singh, “A Deep Learning Framework for Copy-Move Forgery Detection in Digital Images,” Multimedia Tools and Applications, vol. 82, no. 12, pp. 17741-17768, 2023.
[CrossRef] [Google Scholar] [Publisher Link]