Abstract

There are three main steps to detect fake images, namely feature extraction, feature matching, and feature masking based on the similarity between two or more images, or parts of images. The detection accuracy of fraudulent images highly depends on the feature extraction. Thus, the quality of the extracted features plays a key role in the image forgery detection. Multiple feature extraction methods have been proposed for detecting fake images with different level of success, however, not many have the ability to extract geometrical transformation such as rotation and scaling. Hence, to overcome the issue, this paper presents two separate groups of feature extraction, namely the dimensional reduction and keypoint. Initially, we ran a series of experiments to reveal the best feature extraction methods, involving five methods, from both groups in detecting copy-moved image forgeries. Then in our experiments, we implemented the integration of singular value decomposition (SVD) and speeded up robust features (SURF), discrete cosine transform (DCT) and SURF, and discrete wavelet transform (DWT) and SURF, to formulate a more accurate and robust copy-move detection approach. The best performance of copy-move detection was achieved by deploying DWT and SURF. The integration of DWT and SURF solves the rotation and the scaling issues in copy-move image detection with higher high accuracy and shorter execution time.

Keyword

Image forgery, Dimensional reduction, Keypoint, discrete wavelet transform and Speeded up robust features.

Cite this article

Yusoff N, Alamro L

Refference

[1][1]Kumar S, Desai J, Mukherjee S. A fast DCT based method for copy move forgery detection. In second international conference on image information processing 2013 (pp. 649-54). IEEE.

[2][2]Lynch G, Shih FY, Liao HY. An efficient expanding block algorithm for image copy-move forgery detection. Information Sciences. 2013; 239:253-65.

[3][3]Tralic D, Rosin PL, Sun X, Grgic S. Copy-move forgery detection using cellular automata. In cellular automata in image processing and geometry 2014 (pp. 105-25). Springer, Cham.

[4][4]Sunil K, Jagan D, Shaktidev M. DCT-PCA based method for copy-move forgery detection. In ICT and critical infrastructure: proceedings of the annual convention of computer society of india-Vol II 2014 (pp. 577-83). Springer, Cham.

[5][5]Ahmadi M, Ulyanov D, Semenov S, Trofimov M, Giacinto G. Novel feature extraction, selection and fusion for effective malware family classification. In the conference on data and application security and privacy 2016 (pp. 183-94). ACM.

[6][6]Ramya RS, Venugopal KR, Iyengar SS, Patnaik LM. Feature extraction and duplicate detection for text mining: a survey. Global Journal of Computer Science and Technology. 2017.

[7][7]Lei B, Xu G, Feng M, Van der Heijden F, Zou Y, De Ridder D, et al. Classification, parameter estimation and state estimation: an engineering approach using MATLAB. John Wiley & Sons; 2017.

[8][8]Shah FP, Patel V. A review on feature selection and feature extraction for text classification. In international conference on wireless communications, signal processing and networking 2016 (pp. 2264-8). IEEE.

[9][9]Ponti M, Nazaré TS, Thumé GS. Image quantization as a dimensionality reduction procedure in color and texture feature extraction. Neurocomputing. 2016; 173:385-96.

[10][10]Bin YA, Xingming SU, Xianyi C, Zhang J, Xu LI. An efficient forensic method for copy-move forgery detection based on DWT-FWHT. Radioengineering. 2013; 22(4): 1098-105.

[11][11]Zhao J, Guo J. Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Science International. 2013; 233(1-3):158-66.

[12][12]Huang H, Guo W, Zhang Y. Detection of copy-move forgery in digital images using SIFT algorithm. In pacific-Asia workshop on computational intelligence and industrial application 2008 (pp. 272-6). IEEE.

[13][13]Pan X, Lyu S. Detecting image region duplication using SIFT features. In international conference on acoustics, speech and signal processing 2010 (pp. 1706-9). IEEE.

[14][14]Pan X, Lyu S. Region duplication detection using image feature matching. Transactions on Information Forensics and Security. 2010; 5(4):857-67.

[15][15]Bo X, Junwen W, Guangjie L, Yuewei D. Image copy-move forgery detection based on SURF. In international conference on multimedia information networking and security 2010 (pp. 889-92). IEEE.

[16][16]Shukla KK, Tiwari AK. Efficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systems. Springer Science & Business Media; 2013.

[17][17]Kociołek M, Materka A, Strzelecki M, Szczypiński P. Discrete wavelet transform-derived features for digital image texture analysis. In international conference on signals and electronic systems 2001 (pp. 99-104).

[18][18]Chui CK. An introduction to wavelets. Elsevier; 2016.

[19][19]Kanika DN, Sharma K. Comparative analysis of discrete wavelet transform and fast wavelet transform on image compression. International Journal of Engineering Research & Technology. 2012; 1(5):1-7.

[20][20]Chowdhury MM, Khatun A. Image compression using discrete wavelet transform. International Journal of Computer Science Issues. 2012; 9(4):327-30.

[21][21]Ahmed N, Natarajan T, Rao KR. Discrete cosine transform. IEEE Transactions on Computers. 1974; 100(1):90-3.

[22][22]Cook GW, Kalker T. The sparse discrete cosine transform with application to image compression. In picture coding symposium (PCS) 2013 (pp. 9-12). IEEE.

[23][23]Kekre HB, Sarode T, Natu PJ. Color image compression using hybrid Haar-DCT wavelet in different color spaces. Advances in Image and Video Processing. 2014; 2(4):1-1.

[24][24]Salomon D. A concise introduction to data compression. Springer Science & Business Media; 2007.

[25][25]Andrews H, Patterson CL. Singular value decomposition (SVD) image coding. IEEE Transactions on Communications. 1976; 24(4):425-32.

[26][26]Gallier J. Singular value decomposition (SVD) and polar form. In geometric methods and applications 2011 (pp. 367-85). Springer, New York, NY.

[27][27]Lee N, Cichocki A. Big data matrix singular value decomposition based on low-rank tensor train decomposition. In international symposium on neural networks 2014 (pp. 121-30). Springer, Cham.

[28][28]Ientilucci EJ. Using the singular value decomposition. Rochester Institute of Technology, Rochester, New York, United States, Technical Report. 2003.

[29][29]Balaji GN, Subashini TS, Chidambaram N. Cardiac view classification using speed Up robust. Indian Journal of Science and Technology. 2015; 8(S7):1-5.

[30][30]Bay H, Ess A, Tuytelaars T, Van Gool L. Speeded-up robust features (SURF). Computer Vision and Image Understanding. 2008; 110(3):346-59.

[31][31]Guo F, Luo X, Liu Y. Research on feature extraction and match method based on the surf algorithm for mobile augmented reality system. In international industrial informatics and computer engineering conference 2015. Atlantis Press.

[32][32]Alamro L, Yusoff N. Copy-move forgery detection using integrated DWT and SURF. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 2017; 9(1-2):67-71.