References

Alahmadi, A., M. Hussain, H. Aboalsamh, G. Muhammad and G. Bebis et al., 2017. Passive detection of image forgery using DCT and local binary pattern. Signal Image Video Process., 11: 81-88.
CrossRef  |  Direct Link  |  

Ben-Hur, A. and J. Weston, 2010. A Users Guide to Support Vector Machines. In: Data Mining Techniques for the Life Sciences, Carugo, O. and F. Eisenhaber, (Eds.). Humana Press, New York, USA., ISBN:978-1-60327-240-7, pp: 223-239.

Bhatia, N., 2010. Survey of nearest neighbor techniques. Comput. Vision Pattern Recognit., 8: 302-305.
Direct Link  |  

Burvin, P.S. and J.M. Esther, 2014. Analysis of digital image splicing detection. IOSR. J. Comput. Eng., 16: 10-13.

Farid, H., 2009. Image forgery detection. IEEE. Signal Process. Mag., 26: 16-25.
CrossRef  |  Direct Link  |  

Hakimi, F. and H. Mahdi, 2015. Image-splicing forgery detection based on improved LBP and K-nearest neighbors algorithm. Electron. Inf. Plann. J. Electron. Inf. Plann., 3: 381-387.

Hakimi, F., M. Hariri and F. GharehBaghi, 2015. Image splicing forgery detection using local binary pattern and discrete wavelet transform. Proceedings of the 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), November 5-6, 2015, IEEE, Tehran, Iran, ISBN:978-1-4673-6505-5, pp: 1074-1077.

He, Z., W. Lu, W. Sun and J. Huang, 2012. Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recognit., 45: 4292-4299.
CrossRef  |  Direct Link  |  

Huang, D., C. Shan, M. Ardabilian, Y. Wang and L. Chen, 2011. Local binary patterns and its application to facial image analysis: A survey. IEEE Trans. Syst. Man Cybern. Part C: Applic. Rev., 41: 765-781.
CrossRef  |  Direct Link  |  

Ibraheem, N.A., M.M. Hasan, R.Z. Khan and P.K. Mishra, 2012. Understanding color models: A review. ARPN. J. Sci. Technol., 2: 265-275.

Julliand, T., V. Nozick and H. Talbot, 2015. Image noise and digital image forensics. Proceedings of the International Workshop on Digital Watermarking (IWDW 2015), October 7-10, 2015, Springer, Tokyo, Japan, ISBN:978-3-319-31959-9, pp: 3-17.

Kaur, M. and S. Gupta, 2016. A passive blind approach for image splicing detection based on DWT and LBP histograms. Proceedings of the International Symposium on Security in Computing and Communication (SSCC 2016), September 21-24, 2016, Springer, Singapore, ISBN:978-981-10-2737-6, pp: 318-327.

Kingsbury, N. and J. Magarey, 1998. Wavelet Transforms in Image Processing. In: Signal Analysis and Prediction, Prochazka, A., J. Uhlir, P.J.W. Rayner and N.G. Kingsbury (Eds.). Birkhauser, Basel, Switzerland, ISBN:9780817640422, pp: 27-46.

Lakshmi, K.V. and N.S. Kumari, 2018. Survey on naive bayes algorithm. Intl. J. Adv. Res. Sci. Eng., 7: 240-246.
Direct Link  |  

Li, C., Q. Ma, L. Xiao, M. Li and A. Zhang, 2017. Image splicing detection based on Markov features in QDCT domain. Neurocomputing, 228: 29-36.
CrossRef  |  Direct Link  |  

Muhammad, G., M.H. Al-Hammadi, M. Hussain and G. Bebis, 2014. Image forgery detection using steerable pyramid transform and local binary pattern. Mach. Vision Appl., 25: 985-995.
CrossRef  |  Direct Link  |  

Mushtaq, S. and A.H. Mir, 2014. Novel method for image splicing detection. Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), September 24-27, 2014, IEEE, New Delhi, India, ISBN:978-1-4799-3078-4, pp: 2398-2403.

Ng, T.T., S.F. Chang and Q. Sun, 2004. A data set of authentic and spliced image blocks. MSc Thesis, Columbia University, New York, USA.

Ojala, T., M. Pietikainen and T. Maenpaa, 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE. Trans. Pattern Anal. Mach. Intell., 7: 971-987.
CrossRef  |  Direct Link  |  

Porwik, P. and A. Lisowska, 2004. The haar-wavelet transform in digital image processing: Its status and achievements. Mach. Graphics Vision, 13: 79-98.
Direct Link  |  

Potdar, V.M., S. Han and E. Chang, 2005. A survey of digital image watermarking techniques. Proceedings of the 2005 3rd IEEE International Conference on Industrial Informatics (INDIN'05.), August 10-12, 2005, IEEE, New York, USA., ISBN:0-7803-9094-6, pp: 709-716.

Qazi, T., K. Hayat, S.U. Khan, S.A. Madani and I.A. Khan et al., 2013. Survey on blind image forgery detection. IET. Image Process., 7: 660-670.
CrossRef  |  Direct Link  |  

Rey, C. and J.L. Dugelay, 2002. A survey of watermarking algorithms for image authentication. EURASIP. J. Appl. Sig. Process., 2002: 613-621.
CrossRef  |  Direct Link  |  

Saleh, S.Q., M. Hussain, G. Muhammad and G. Bebis, 2013. Evaluation of image forgery detection using multi-scale weber local descriptors. Proceedings of the International Symposium on Visual Computing (ISVC 2013), July 29-31, 2013, Springer, Berlin, Heidelberg, Germany, ISBN:978-3-642-41938-6, pp: 416-424.

Shih, F.Y., 2012. Multimedia Security: Watermarking, Steganography and Forensics. CRC Press, Boca Raton, Florida, USA., ISBN:9781439873311, Pages: 386.

Sree, S., 2015. Survey on extraction of texture based features using local binary pattern. Intl. J. Eng. Res. Technol., 4: 334-338.
Direct Link  |  

Zhang, Y., C. Zhao, Y. Pi, S. Li and S. Wang, 2015. Image‐splicing forgery detection based on local binary patterns of DCT coefficients. Secur. Commun. Netw., 8: 2386-2395.
CrossRef  |  Direct Link  |  

Zhang, Y., J. Goh, L.L. Win and V.L. Thing, 2016. Image Region Forgery Detection: A Deep Learning Approach. In: Proceedings of the Singapore Cyber-Security Conference (SG-CRC), Mathur, A. and A. Roychoudhury (Eds.). IOS Press, Amsterdam, Netherlands, ISBN:9781614996163, pp: 1-11.