Improving the Detection Rate of Forgery JPEG Images Based on Combining Histogram Features and Discrete Wavelet Transform (DWT) with the Use of Support-Vector Machine

Mohammadi, Azam; Navabifar, Farhad

Document Type : Review Article

Authors

Department of Electrical Engineering, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Iran

Abstract

Manipulating digital images is not often a difficult task due to the rapid development of software and image manipulation techniques. Hence, there is no need for professional skills or training. When used as an artistic tool, it is completely harmless, but when these images can be presented in judicial system as the evidence or for the creation of political associations, as well as using them in legal documents, electronic money circulation or press, in these cases, the distinction between an original image and a forgery image is very important. In order to solve the problem in this research, by using a discrete wavelet transform (DWT), which is performed by decomposing a signal into smaller and smaller details, as well as the use of periodic patterns in the histogram generated by double compression with different coefficients, significant improvements were made in terms of reducing computations and increasing the detection rate of forging areas. Most of the proposed methods for detecting image forgery use a feature extraction model from a valid and manipulated dataset and then classify them using machine learning with the aim of optimizing accuracy. The method used in paper, using the SVM classification identifies image forgery and then identifies the forging area after it detects the falsification or originality of the image. The results of this study indicate 97.98% accuracy in the Columbia database and 98.1% in the IFS-TC database.

Keywords

References

[1] J. Dong, W.W., "CASIA Tampered Image Detection Evaluation Database (CASIA TIDE v2.0)[Online]", Available: http://forensics.idealtest.org:8080/index_v2.html. Chinese Academy of Sciences, 2010.

[2] Rey, C. and J. LD, "A survey of watermarking algorithms for image authentication", EURASIP Journal on Ap plied Signal Processing, 6: pp. , 2002.

[3] Ng, T.T., C.Y. Lin, and Q. Sun, "Passive-blind im-age forensics", in Multimedia Security Technologies for Digital Rights, chapter 06. Elsvier, 2006.

[4] Ng, T.T. and Q. Sun, "Blind detection of photomontage using higher order statistics", in IEEE International Symposium on Circuits and Systems, 2004.

[5] Luka´s, J. and M. Goljan, "Detecting digital im-age forgeries using sensor pattern noise", in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, pp. 11, 2006.

[6] A. C. Popescu, "Exposing digital forgeries in color filter array interpolated images", IEEE Transactions on Signal Processing, (53(10, pp. 12, 2005.

[7] Dirik, A.E. and N. Memon, "Image tamper detection based on demosaicing artifacts", in IEEE International Conference on Image Processing (ICIP), pp. 4, 2009.

[8] Johnson, M.K., "Exposing digital forgeries by detecting inconsistencies in lighting", in ACM Multimedia and Security Workshop, pp. 10, 2005.

[9] M. K. Johnson, H.F., "Exposing digital forgeries in complex lighting environments", IEEE Transactions on In-formation Forensics and Security, 2(3), pp. 11, 2007.

[10] Ng, T.T. and Q. Sun, "A data set of authentic and spliced image blocks", Tech. Rep., DVMM, Columbia University, Dataset: http://www.ee.columbia.edu/ln/dvmm/do wnloads/AuthSplicedDataSet/photographers.htm., 2004.

[11] Shi, Y.Q. and G. Xuan, "Steganalysis versus splicing detection", in International Workshop on Digital Watermarking, 2007.

[12] Ghorbani, M., M.Firozmand, and A. Faraahi, "DWT-DCT (QCD) based copy-move image forgery detection", in 18th IEEE International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 14, 2011.

[13] Gh. Muhammad, M.H.A.-H., and M. H. George Bebis, "Image forgery detection using steerable pyramid transform and local binary pattern", Springer, 2013.

[14] Chi-Man Pun, Xiao-Chen Yuan, and Xiu-Li Bi, "Image Forgery Detection Using Adaptive Over-Segmentation and Feature Point Matching", IEEE Transactions on Information Forensics and Security, 2015.

[15] Archana V. Mire, S. B. Dhok, N. J. Mistry and P. D. Porey, "Factor Histogram based Forgery Localization in Double Compressed JPEG Images", Procedia Computer Science 54, pp. 690–696, 2015.

[16] L. G. Birajdar, e., "Passive Image Manipulation Detection Using Wavelet Transform and Support Vector Machine Classifier", Springer, 2016.

[17] Chi-Man Pun, X.-C. Yuan, and Xiu-Li Bi, "Multi-scale noise estimation for image splicing forgery detection", Elsevier Inc., 2016.

[18] Y. Obara, Y. Niwa, and SH. Wada, "Detection and Identification of Image Manipulation Based on Reversible Histogram shift.",Wiley Periodicals, Inc., 2017.

[19] A. Kuznetsov and V. Myasnikov, "A new copy-move forgery detection algorithm using image preprocessing procedure", Elsevier Inc., 2017.

[20] Khizar Hayat, Tanzeela Qazi, "Forgery detection in digital images via discrete wavelet and discrete cosine transforms", Computers and Electrical Engineering, elsevier, pp.1-11, 2017

[21] N. Alipour and A. Behrad, "Forgery and Double Compression Detection in Digital Images using Combined Features of Quantization Effects on DCT Coefficients", Tabriz Jouornal of Engineering Electrical, 47(2), 2017.

[22] Srivastava, D.K. and L. Bhambhu, "Data classification using support vector machine", Journal of Theoretical and Applied Information Technology, 12, pp. 8, 2010.

[23] http://www.ee.columbia.edu/ln/dvmm/downloads/authsplcuncmp/dlform.html

[24] http://ifc.recod.ic.unicamp.br/fc.submission

Majlesi Journal of Electrical Engineering

Improving the Detection Rate of Forgery JPEG Images Based on Combining Histogram Features and Discrete Wavelet Transform (DWT) with the Use of Support-Vector Machine

References

References

Volume 13, Issue 4 - Serial Number 4
December 2019
Pages 111-121

Improving the Detection Rate of Forgery JPEG Images Based on Combining Histogram Features and Discrete Wavelet Transform (DWT) with the Use of Support-Vector Machine

References

References

Volume 13, Issue 4 - Serial Number 4December 2019Pages 111-121

Volume 13, Issue 4 - Serial Number 4
December 2019
Pages 111-121