Abstract
Daily millions of images are uploaded and download to the web, as a result the data is available in the paperless form in the computer system for organization. Nowadays, with the help of powerful computer software such as Photoshop and Corel Draw, it is very easy to alter the contents of the authenticated image without leaving any clues. This led to a big problem due to the negative impact of image splicing. It is highly recommended to develop image tampering detection technique to recognize the authentic and temper images. In this paper, we propose an enhanced technique for blind images splicing by combing Discrete Cosine Transform Domain (DTC) and Markov feature in the spatial domain. Moreover, Principal Component Analysis (PCA) is used to select the most significant features. Finally, Support Vector Machine (SVM) is applied to classify the image as being tempered or genuine on the publicly available dataset using ten-fold cross-validation. By applying different statistical techniques, the results showed that the proposed technique performs better than other available detection techniques in the literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abrahim, A.R., Rahim, M.S.M., Sulong, G.B.: Splicing image forgery identification based on artificial neural network approach and texture features. Clust. Comput., 1–14 (2018)
Li, C., et al.: Image splicing detection based on Markov features in QDCT domain. Neurocomputing 228, 29–36 (2017)
Zeng, H., et al.: Image splicing localization using PCA-based noise level estimation. Multimed. Tools Appl. 76(4), 4783–4799 (2017)
Javaid, Q., Arif, M., Awan, D., Shah, M.: Efficient facial expression detection by using the Adaptive-Neuro-Fuzzy-Inference-System and the Bezier curve. Sindh Univ. Res. J.-SURJ (Sci. Ser.) 48(3) (2016)
Javaid, Q., Arif, M., Shah, M.A., Nadeem, M.: A hybrid technique for De-Noising multi-modality medical images by employing cuckoo’s search with curvelet transform. Mehran Univ. Res. J. Eng. Technol. 37(1), 29 (2018)
ur Rahman, H., Azzedin, F., Shawahna, A., Sajjad, F., Abdulrahman, A.S.: Performance evaluation of VDI environment. In: 2016 Sixth International Conference on Innovative Computing Technology (INTECH), Dublin, pp. 104–109 (2016)
Jingwei, H., Dake, Z., Xin, Y., Qingxian, W.: Image splicing detection based on local mean decomposition and moment features. Electron. Meas. Technol. 4, 033 (2017)
Arif, M., Abdullah, N.A., Phalianakote, S.K., Ramli, N., Elahi, M.: Maximizing information of multimodality brain image fusion using curvelet transform with genetic algorithm. In: 2014 International Conference on Computer Assisted System in Health (CASH), pp. 45–51. IEEE (2014)
El-Alfy, E.S.M., Qureshi, M.A.: Combining spatial and DCT based Markov features for enhanced blind detection of image splicing. Pattern Anal. Appl., 1–11 (2014)
Shi, Y.Q., Chen, C., Chen, W.: A natural image model approach to splicing detection. In: Proceedings of the 9th Workshop on Multimedia and Security, pp. 51–62 (2007)
Zhongwei, H., Lu Wei Sun, W.: Digital image splicing detection based on markov features in DCT and DWT domain. Pattern Recognit. 45(12), 4292–4299 (2012)
Qureshi, M.A., Deriche, M.: A bibliography of pixel-based blind image forgery detection techniques. Signal Process. Image Commun. 39, 46–74 (2015)
Zhao, X., Wang, S., Li, S., Li, J.: A comprehensive study on third order statistical features for image splicing detection. In: Digit Forensics Watermarking, pp. 243–256 (2012)
Ng, T.-T., Chang, S.-F., Sun, Q.: A data set of authentic and spliced image blocks, Columbia University, ADVENT Technical Report, pp. 203–204 (2004)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)
Srivastava, A., Lee, A.B., Simoncelli, E.P., Zhu, S.-C.: On advances in statistical modeling of natural images. J. Math. Imaging Vis. 18(1), 17 (2003)
Amali, G.B., Bhuyan, S.: Aju: design of image enhancement filters using a novel parallel particle swarm optimisation algorithm. Int. J. Adv. Intell. Parad. 9(5–6), 576–588 (2017)
Chizari, H., et al.: Computer forensic problem of sample size in file type analysis. Int. J. Adv. Intell. Parad. 11(1–2), 58–74 (2018)
Muhammad, A., Guojun, W.: Segmentation of calcification and brain hemorrhage with midline detection. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC). IEEE (2017)
Ai, D., et al.: A multi-agent system architecture to classify colour images. Int. J. Adv. Intell. Parad. 5(4), 284–298 (2013)
Acknowledgement
This work was supported from the project GUSV, “Intelligent techniques for medical applications using sensor networks”, project no. 10BM/2018, financed by UEFISCDI, Romania under the PNIII framework.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
ur Rhhman, H. et al. (2021). Comparative Analysis of Various Image Splicing Algorithms. In: Balas, V., Jain, L., Balas, M., Shahbazova, S. (eds) Soft Computing Applications. SOFA 2018. Advances in Intelligent Systems and Computing, vol 1222. Springer, Cham. https://doi.org/10.1007/978-3-030-52190-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-52190-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-52189-9
Online ISBN: 978-3-030-52190-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)