A Splicing Technique for Image Tampering using Morphological Operations

Achmad Fanany Onnilita Gaffar, Supriadi Supriadi, Arief Bramanto Wicaksono Saputra, Rheo Malani, Agusma Wajiansyah

Abstract


Image tampering is one part of the field of image editing or manipulation that changes certain parts of the graphic content of a given image. There are several techniques commonly used for image tampering, such as splicing, copy-move, retouching, etc. Splicing is a type of image tampering technique that combines two different images, replacing particular objects, skewing, rotation, etc. This study applies the splicing technique to image tampering using morphological operations.  Morphology is a collection of image processing operations that process images based on their shape. The aim of this study is to replace particular objects in an original image with other objects that are similar to another selected image.  In this study, we try to replace the ball object in the original image with another ball object from another image

Keywords


Image manipulation; Image tampering; Splicing technique; Morphological operations

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References


P. Li and Y. Zhao, "A Simple Encryption Algorithm for Quantum Color Image," International Journal of Theoretical Physics, vol. 56, pp. 1961-1982, 2017.

P. Chakravorty, "What Is a Signal?," IEEE Signal Processing Magazine, 2018.

L. G. Hafemann, R. Sabourin, and L. S. Oliveira, "Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks," International Joint Conference on Neural Networks (IJCNN),Vancouver, BC, Canada, IEEE, 2016.

E. Hussain, A. Hannan, and K. Kashyap, "A Zoning based Feature Extraction method for Recognition of Handwritten Assamese Characters " InternatIonal Journal of Computer SCIenCe and teChnology, vol. 6, 2015.

M. R. Kaur and M. P. Choudhary, "Handwritten Signature Verification Based on Surf Features Using HMM," International Journal of Computer Science Trends and Technology (IJCST), vol. 3, 2015.

F. S. Mohamad, F. M. Alsuhimat, Mohamad Afendee Mohamed, M. Mohamad, and A. A. Jamal, "Detection and Feature Extraction for Images Signatures," International Journal of Engineering & Technology, vol. 7, 2018.

M. A. Mohamad, D. Nasien, H. Hassan, and H. Haron, "A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition," (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 6, 2015.

S. T. Panchal and V. V.Yerigeri, "Offline signature verification based on geometric feature extraction using artificial neural network," IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), vol. 13, 2018.

A. J. S, P. C. R, T. V, S. K, K. B. Raja, D. Anvekar, V. K. R, S. S. Iyengar, L. M. Patnaik, A. C, and P. K. S, "OSPCV: Off-line Signature Verification using Principal Component Variances " IOSR Journal of Computer Engineering (IOSR-JCE), vol. 7, 2015.

J. J. d. M. Sá Junior, L. C. Ribas, and O. M. Bruno, "Randomized neural network based signature for dynamic texture classification," Expert Systems with Applications, vol. 135, pp. 194-200, 2019.

Y. Serdouk, H. Nemmour, and Y. Chibani, "Handwritten signature verification using the quad-tree histogram of templates and a Support Vector-based artificial immune classification," Image and Vision Computing, vol. 66, pp. 26-35, 2017.

P. Wei, H. Li, and P. Hu, "Inverse Discriminative Networks for Handwritten Signature Verification," Computer Vision and Pattern Recognition (CVPR), IEEE Xplore, 2019.

H.-H. Chao, C.-W. Yeh, C. F. Hsu, L. Hsu, and S. Chi, "Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure," Applied Sciences, vol. 9, p. 3496, 2019.

H. Ding, X. Jiang, B. Shuai, A. Q. Liu, and G. Wang, "Context Contrasted Feature and Gated Multi-scale Aggregationfor Scene Segmentation," CVF Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2018.

X. Shen and A. I. Zayed, "Continuous Non-negative wavelets and their use in Density Estimation," researchgate, publication no.267003753, 2013.

L. Xing, L. Cai, H. Zeng, J. Chen, J. Zhu, and J. Hou, "A multi-scale contrast-based image quality assessment model for multi-exposure image fusion," Signal Processing, vol. 145, pp. 233-240, 2018.

L. Yang, X. Chen, and L. Tao, "Acoustic scene classification using Multi-Scale Features," Detection and Classification of Acoustic Scenes and Events, Surrey, UK, 2018.

G. Liang, S. Ren, and F. Dong, "An EIT image segmentation method based on projection distance minimization," 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China 2017.

R. Wei, F. Zhou, B. Liu, B. Liang, B. Guo, and X. Xu, "A CNN Based Volumetric Imaging Method With Single X-ray Projection," 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China, 2017.

V. Conti, "Biometric Authentication Overview: a Fingerprint Recognition Sensor Description," International Journal of Biosensors & Bioelectronics, vol. 2, 2017.

S. Theodoridis and K. Koutroumbas, Pattern Recognition - Second Edition. America: Academic Press - An imprint of Elsevier, 2003.

S. Mushtaq and A. H. Mir, "Image Copy Move Forgery Detection: A Review," International Journal of Future Generation Communication and Networking, vol. 11, pp. 11-22, 2018.

L. Zheng, Y. Zhang, and V. L. L. Thing, "A survey on image tampering and its detection in real-world photos," Journal of Visual Communication and Image Representation, vol. 58, pp. 380-399, 2019.

S. Mushtaq and A. H. Mir, "Digital Image Forgeries and Passive Image Authentication Techniques: A Survey," International Journal of Advanced Science and Technology, vol. 73, pp. 15-32, 2014.

O. M. Al-Qershi and B. E. Khoo, "Passive detection of copy-move forgery in digital images: state-of-the-art," Forensic Sci Int, vol. 231, pp. 284-95, Sep 10 2013.

AtifShah and E.-S. M. El-Alfy, "Image Splicing Forgery Detection Using DCT Coefficients with Multi-Scale LBP," 2018 International Conference on Computing Sciences and Engineering (ICCSE), Kuwait City, Kuwait, 2018.

A. B and N. George, "A robust technique for splicing detection in tampered blurred images," 2017 International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, India, 2017.

N. Pham, J.-W. Lee, G.-R. Kwon, and C.-S. Park, "Hybrid Image-Retrieval Method for Image-Splicing Validation," Symmetry, vol. 11, p. 83, 2019.

H. u. Rhhman, M. Arif, S. Al-Azani, and V. E. Balas, "Comparative Analysis of various Image Splicing Algorithms " ResearchGate/publication/335030672, 2019.

N. Al Madeed, Z. Awan, and S. Al Madeed, "Image Quality Assessment - A Survey of Recent Approaches," pp. 143-156, 2018.

M. A. B. Siddique, R. B. Arif, and M. M. R. Khan, "Digital Image Segmentation in Matlab: A Brief Study on Otsu’s Image Thresholding," Creative Commons CC BY license, doi:10.20944/preprints201811.0530.v2, 2019.




DOI: https://doi.org/10.31763/simple.v1i2.4

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Copyright (c) 2019 Achmad Fanany Onnylita Gaffar, Supriadi Supriadi, Arief Bramanto Wicaksono Putra, Rheo Malani, Agusma Wajiansyah

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Signal and Image Processing Letters

ISSN Online: 2714-6677 | Print: 2714-6669
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
Website : https://simple.ascee.org/index.php/simple/
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Email 2 : azhari@ascee.org


 

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