A bibliography of pixel-based blind image forgery detection techniques

Signal Processing: Image Communication

Volumn 39, Issue , 2015, Pages 46-74

  Qureshi, Muhammad Ali ^a   Deriche, Mohamed ^a  

^a KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS   (Saudi Arabia)

Author keywords

Copy move forgery; Forgery detection methods; Image forensics; Image quality; Image retouching; Image splicing; Image tampering detection

Indexed keywords

GOVERNMENT DATA PROCESSING; IMAGE QUALITY; INFORMATION SERVICES; MULTIMEDIA SYSTEMS; PIXELS; SURVEYS;

COPY-MOVE FORGERIES; FORGERY DETECTIONS; IMAGE FORENSICS; IMAGE RETOUCHING; IMAGE SPLICING; IMAGE TAMPERING;

DIGITAL FORENSICS; PIXELS;

EID: 84947744414     PISSN: 09235965     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.image.2015.08.008     Document Type: Article

References (229)

1. P. Lester Photojournalism: An Ethical Approach 1991 Lawrence Erlbaum Associates Inc. Hillsdale, NJ, USA
2. Photo tampering throughout history, (http://http://www.fourandsix.com/photo-tampering-history?currentPage=4), Accessed: 2015-03-27, 2004.
3. M.C. Stamm, M. Wu, and K. Liu Information forensics: an overview of the first decade IEEE Access 1 2013 167 200
4. J.A. Redi, W. Taktak, and J.-L. Dugelay Digital image forensics: a booklet for beginners Multimed. Tools Appl. 51 1 2011 133 162
5. H. Farid Image forgery detection: a survey IEEE Signal Process. Mag. 26 2 2009 16 25
6. B. Mahdian, and S. Saic A bibliography on blind methods for identifying image forgery Signal Process.: Image Commun. 25 6 2010 389 399
7. A. Piva An overview on image forensics ISRN Signal Process. 2013 1 22
8. T. Qazi, K. Hayat, S.U. Khan, S.A. Madani, I.A. Khan, J. Kołodziej, H. Li, W. Lin, K.C. Yow, and C.-Z. Xu Survey on blind image forgery detection IET Image Process. 7 7 2013 660 670
9. O.M. Al-Qershi, and B.E. Khoo Passive detection of copy-move forgery in digital images: state-of-the-art Forensic Sci. Int. 231 1-3 2013 284 295
10. G.K. Birajdar, and V.H. Mankar Digital image forgery detection using passive techniques: a survey Digit. Investig. 10 3 2013 226 245
11. M. Ali Qureshi, M. Deriche, A review on copy move image forgery detection techniques, in: 11th International Multi-Conference on Systems, Signals & Devices (SSD), IEEE, Barcelona, Spain, 2014, pp. 1-5.
12. K. Eismann Photoshop Restoration and Retouching 2005 Peachpit Press
13. 40 Amazing Before and After Photo Retouching Photos, (http://10steps.sg/inspirations/artworks/40-cool-before-and-after-photo-retouching-photos), Accessed: 2015-03-27, March 2013.
14. Jopseph Casers: Sin Photoshop Y Con Photoshop, (http://merengala.blogspot.com/2010/12/sin-photoshop-y-con-photoshop.html), Accessed: 2015-03-27, April 2013.
15. A.J. Fridrich, B.D. Soukal, A.J. Lukáš, Detection of copy-move forgery in digital images, in: Digital Forensic Research Workshop (DFRWS), Citeseer, 2003.
16. M. Nizza, P.J. Lyons, In an iranian image, a missile too many, (http://thelede.blogs.nytimes.com/2008/07/10/in-an-iranian-image-a-missile-too-many/), Accessed: 2015-02-21, 2008.
17. H. Farid, Detecting Digital Forgeries Using Bispectral Analysis, Technical Report AIM-1657, AI Lab, Massachusetts Institute of Technology, 1999.
18. J. Dong, W. Wang, CASIA tampered image detection evaluation (TIDE) database, v1. 0 and v2. 0, (http://forensics.idealtest.org:8080/), Accessed: 2014-01-17, 2011.
19. J. Dong, W. Wang, T. Tan, CASIA image tampering detection evaluation database, in: IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), IEEE, Beijing, China, 2013, pp. 422-426.
20. I.J. Cox, M.L. Miller, and J.A. Bloom Digital Watermarking 2001 Morgan Kaufmann Orlando, FL, USA
21. M. Barni, F. Bartolini, Watermarking Systems Engineering: Enabling Digital Assets Security and Other Applications, Signal Processing and Communications, Marcel Dekker, New York, USA, 2004.
22. I. Cox, M. Miller, J. Bloom, J. Fridrich, and T. Kalker Digital WaterMarking and Steganography 2nd edition 2008 Morgan Kaufmann Francisco, CA, USA
23. A.J. Menezes, P.C. Van Oorschot, and S.A. Vanstone Handbook of Applied Cryptography 1st edition 1996 CRC Press Boca Raton, FL, USA
24. D. Fu, Y.Q. Shi, W. Su, A generalized Benford's law for JPEG coefficients and its applications in image forensics, in: Electronic Imaging, vol. 6505, International Society for Optics and Photonics, San Jose, CA, USA, 2007, p. 65051L.
25. B. Li, Y.Q. Shi, J. Huang, Detecting doubly compressed JPEG images by using mode based first digit features, in: 10th Workshop on Multimedia Signal Processing, IEEE, Cairns, QLD, Australia, 2008, pp. 730-735.
26. T. Bianchi, and A. Piva Image forgery localization via block-grained analysis of JPEG artifacts IEEE Trans. Inf. Forensics Secur. 7 3 2012 1003 1017
27. T. Bianchi, and A. Piva Detection of nonaligned double JPEG compression based on integer periodicity maps IEEE Trans. Inf. Forensics Secur. 7 2 2012 842 848
28. S. Milani, M. Tagliasacchi, S. Tubaro, Discriminating multiple JPEG compression using first digit features, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Kyoto, Japan, 2012, pp. 2253-2256.
29. C. Pasquini, G. Boato, F. Perez-Gonzalez, Multiple JPEG compression detection by means of Benford-Fourier coefficients, in: International Workshop on Information Forensics and Security (WIFS), IEEE, Atlanta, GA, USA, 2014, pp. 113-118.
30. Z. Fan, and R.L. De Queiroz Identification of bitmap compression history: JPEG detection and quantizer estimation IEEE Trans. Image Process. 12 2 2003 230 235
31. M.K. Johnson, H. Farid, Exposing digital forgeries through chromatic aberration, in: 8th Workshop on Multimedia and Security, ACM, New York, NY, USA, 2006, pp. 48-55.
32. A. Swaminathan, M. Wu, and K.R. Liu Nonintrusive component forensics of visual sensors using output images IEEE Trans. Inf. Forensics Secur. 2 1 2007 91 106
33. J. Lukas, J. Fridrich, and M. Goljan Digital camera identification from sensor pattern noise IEEE Trans. Inf. Forensics Secur. 1 2 2006 205 214
34. A.E. Dirik, N.D. Memon, Image tamper detection based on demosaicing artifacts., in: International Conference on Image Processing (ICIP), IEEE, San Diego, CA, USA, 2009, pp. 1497-1500.
35. M. Chen, J. Fridrich, M. Goljan, and J. Lukáš Determining image origin and integrity using sensor noise IEEE Trans. Inf. Forensics Secur. 3 1 2008 74 90
36. S. Bayram, H. Sencar, N. Memon, I. Avcibas, Source camera identification based on CFA interpolation, in: International Conference on Image Processing (ICIP), vol. 3, IEEE, Genova, Italy, 2005, p. III-69.
37. M.K. Johnson, H. Farid, Exposing digital forgeries by detecting inconsistencies in lighting, in: 7th Workshop on Multimedia and Security, ACM, Geneva, Switzerland, 2005, pp. 1-10.
38. M.K. Johnson, and H. Farid Exposing digital forgeries in complex lighting environments IEEE Trans. Inf. Forensics Secur. 2 3 2007 450 461
39. E. Kee, H. Farid, Exposing digital forgeries from 3-D lighting environments, in: International Workshop on Information Forensics and Security (WIFS), IEEE, Seattle, WA, USA, 2010, pp. 1-6.
40. M.K. Johnson, H. Farid, Detecting photographic composites of people, in: 6th International Workshop on Digital Watermarking (IWDW), Springer, Guangzhou, China, 2007, pp. 19-33.
41. M.K. Johnson, H. Farid, Metric Measurements on a Plane from a Single Image, Technical Report TR2006-579, Department of Computer Science, Dartmouth College, 2006.
42. W. Zhang, X. Cao, Z. Feng, J. Zhang, P. Wang, Detecting photographic composites using two-view geometrical constraints, in: International Conference on Multimedia and Expo (ICME), 2009, pp. 1078-1081.
43. L. Li, S. Li, H. Zhu, S.-C. Chu, J.F. Roddick, and J.-S. Pan An efficient scheme for detecting copy-move forged images by Local Binary Patterns J. Inf. Hiding Multimed. Signal Process. 4 1 2013 46 56
44. B. Mahdian, and S. Saic Detection of copy-move forgery using a method based on blur moment invariants Forensic Sci. Int. 171 2 2007 180 189
45. J.-W. Wang, G.-J. Liu, Z. Zhang, Y. Dai, and Z. Wang Fast and robust forensics for image region-duplication forgery Acta Autom. Sin. 35 12 2009 1488 1495
46. G. Liu, J. Wang, S. Lian, and Z. Wang A passive image authentication scheme for detecting region-duplication forgery with rotation J. Netw. Comput. Appl. 34 5 2011 1557 1565
47. S.-J. Ryu, M.-J. Lee, H.-K. Lee, Detection of copy-rotate-move forgery using Zernike moments, in: Information Hiding, Springer, Berlin, Heidelberg, 2010, pp. 51-65.
48. S.-J. Ryu, M. Kirchner, M.-J. Lee, and H.-K. Lee Rotation invariant localization of duplicated image regions based on Zernike moments IEEE Trans. Inf. Forensics Secur. 8 8 2013 1355 1370
49. M.A. Qureshi, M. Deriche, A fast no reference image quality assessment using laws texture moments, in: Global Conference on Signal and Information Processing (GlobalSIP), IEEE, Atlanta, GA, USA, 2014, pp. 979-983.
50. W. Luo, J. Huang, G. Qiu, Robust detection of region-duplication forgery in digital image, in: 18th International Conference on Pattern Recognition, (ICPR), vol. 4, IEEE, Hong Kong, 2006, pp. 746-749.
51. H.-J. Lin, C.-W. Wang, Y.-T. Kao, and et al. Fast copy-move forgery detection WSEAS Trans. Signal Process. 5 5 2009 188 197
52. V.K. Singh, and R. Tripathi Fast and efficient region duplication detection in digital images using sub-blocking method Int. J. Adv. Sci. Technol. 35 2011 93 102
53. V. Christlein, C. Riess, E. Angelopoulou, On rotation invariance in copy-move forgery detection, in: International Workshop on Information Forensics and Security (WIFS), IEEE, Seattle, WA, USA, 2010, pp. 1-6.
54. S. Bravo-Solorio, and A.K. Nandi Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics Signal Process. 91 8 2011 1759 1770
55. S. Bravo-Solorio, A.K. Nandi, Exposing duplicated regions affected by reflection, rotation and scaling, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Prague, Czech Republic, 2011, pp. 1880-1883.
56. D.G. Lowe, Object recognition from local scale-invariant features, in: 7th International Conference on Computer Vision (ICCV), vol. 2, IEEE, Kerkyra, Greece, 1999, pp. 1150-1157.
57. H. Huang, W. Guo, Y. Zhang, Detection of copy-move forgery in digital images using SIFT algorithm, in: Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA), vol. 2, IEEE, Wuhan, China, 2008, pp. 272-276.
58. I. Amerini, L. Ballan, R. Caldelli, A. del Bimbo, G. Serra, Geometric tampering estimation by means of a sif-based forensic analysis, in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, TX, USA, Dallas, TX, USA, 2010, pp. 1702-1705.
59. I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra A SIFT-based forensic method for copy-move attack detection and transformation recovery IEEE Trans. Inf. Forensics Secur. 6 3 2011 1099 1110
60. X. Pan, and S. Lyu Region duplication detection using image feature matching IEEE Trans. Inf. Forensics Secur. 5 4 2010 857 867
61. X. Pan, S. Lyu, Detecting image region duplication using SIFT features, in: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, TX, USA, 2010, pp. 1706-1709.
62. M.A. Fischler, and R.C. Bolles Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography Commun. ACM 24 6 1981 381 395
63. I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, L. Del Tongo, and G. Serra Copy-move forgery detection and localization by means of robust clustering with J-Linkage Signal Process.: Image Commun. 28 6 2013 659 669
64. M. Jaberi, G. Bebis, M. Hussain, and G. Muhammad Accurate and robust localization of duplicated region in copy-move image forgery Mach. Vis. Appl. 25 2 2014 451 475
65. B. Shivakumar, and S.S. Baboo Detection of region duplication forgery in digital images using SURF Int. J. Comput. Sci. Issues 8 4 2011 199 205
66. B. Shivakumar, and S.S. Baboo Automated forensic method for copy-move forgery detection based on Harris Interest Points and SIFT descriptors Int. J. Comput. Appl. 27 3 2011 9 17
67. X. Bo, W. Junwen, L. Guangjie, D. Yuewei, Image copy-move forgery detection based on SURF, in: International Conference on Multimedia Information Networking and Security (MINES), IEEE, Nanjing, Jiangsu, China, 2010, pp. 889-892.
68. E. Ardizzone, A. Bruno, G. Mazzola, Copy-move forgery detection via texture description, in: 2nd ACM Workshop on Multimedia in Forensics, Security and Intelligence, ACM, NY, USA, 2010, pp. 59-64.
69. Y. Huang, W. Lu, W. Sun, and D. Long Improved DCT-based detection of copy-move forgery in images Forensic Sci. Int. 206 1 2011 178 184
70. S. Khan, and A. Kulkarni Reduced time complexity for detection of copy-move forgery using discrete wavelet transform Int. J. Comput. Appl. 6 7 2010 31 36
71. M.K. Bashar, K. Noda, N. Ohnishi, H. Kudo, T. Matsumoto, Y. Takeuchi, Wavelet-based multiresolution features for detecting duplications in images, in: Conference on Machine Vision and Intelligence, 2007, pp. 264-267.
72. S. Bayram, H.T. Sencar, N. Memon, An efficient and robust method for detecting copy-move forgery, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Taipei, Taiwan, 2009, pp. 1053-1056.
73. G. Muhammad, M. Hussain, and G. Bebis Passive copy move image forgery detection using undecimated dyadic wavelet transform Digit. Investig. 9 1 2012 49 57
74. L. Li, S. Li, J. Wang, Copy-move forgery detection based on PHT, in: World Congress on Information and Communication Technologies (WICT), IEEE, Trivandrum, India, 2012, pp. 1061-1065.
75. Y. Li Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching Forensic Sci. Int. 224 1-3 2013 59 67
76. W. Li, N. Yu, Rotation robust detection of copy-move forgery, in: 17th International Conference on Image Processing (ICIP), IEEE, Hong Kong, 2010, pp. 2113-2116.
77. Q. Wu, S. Wang, X. Zhang, Detection of image region-duplication with rotation and scaling tolerance, in: International Conference on Computer and Computational Intelligence (ICCCI), Springer, Kaohsiung, Taiwan, 2010, pp. 100-108.
78. A.C. Popescu, H. Farid, Exposing digital forgeries by detecting duplicated image regions, Technical Report TR2004-515, Department of Computer Science, Dartmouth College, Hanover, New Hampshire, 2004.
79. M.K.V. Hulmukhe, N. KKWIEER, S. Sane, M.A.A. Borse, Exploring duplicated regions in natural images, in: International Conference in Computational Intelligence (ICCIA), 2012.
80. M. Bashar, K. Noda, N. Ohnishi, and K. Mori Exploring duplicated regions in natural images IEEE Trans. Image Process. 99 2010 1 40
81. X. Kang, S. Wei, Identifying tampered regions using singular value decomposition in digital image forensics, in: International Conference on Computer Science and Software Engineering, vol. 3, IEEE, Wuhan, China, 2008, pp. 926-930.
82. Z. Ting, W. Rang-ding, Copy-move forgery detection based on svd in digital image, in: 2nd International Congress on and Signal Processing (CISP), IEEE, Tianjin, China, 2009, pp. 1-5.
83. H.-C. Hsu, M.-S. Wang, Detection of copy-move forgery image using Gabor descriptor, in: International Conference on Anti-Counterfeiting, Security and Identification (ASID), IEEE, Taipei, Taiwan, 2012, pp. 1-4.
84. F. Gharibi, J. RavanJamjah, F. Akhlaghian, B.Z. Azami, J. Alirezaie, Robust detection of copy-move forgery using texture features, in: 19th Iranian Conference on Electrical Engineering (ICEE), IEEE, Tehran, Iran, 2011, pp. 1-4.
85. J. Zhang, Z. Feng, Y. Su, A new approach for detecting copy-move forgery in digital images, in: 11th International Conference on Communication Systems (ICCS), IEEE, Guangzhou, China, 2008, pp. 362-366.
86. M. Zimba, and S. Xingming DWT-PCA (EVD) based copy-move image forgery detection Int. J. Digit. Content Technol. Appl. 5 1 2011 251 258
87. G. Li, Q. Wu, D. Tu, S. Sun, A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD, in: International Conference on Multimedia and Expo (ICME), IEEE, Beijing, China, 2007, pp. 1750-1753.
88. Y. Ke, R. Sukthankar, PCA-SIFT: a more distinctive representation for local image descriptors, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (CVPR), vol. 2, IEEE, Washington, DC, USA, 2004, pp. II-506.
89. M. Ghorbani, M. Firouzmand, A. Faraahi, DWT-DCT (QCD) based copy-move image forgery detection, in: 18th International Conference on Systems, Signals and Image Processing (IWSSIP), IEEE, Sarajevo, Bosnia, 2011, pp. 1-4.
90. M. Zahra Image duplication forgery detection using two robust features Res. J. Recent Sci. 1 12 2012 1 6
91. G. Muhammad, M.S. Hossain, Robust copy-move image forgery detection using undecimated wavelets and Zernike moments, in: 3rd International Conference on Internet Multimedia Computing and Service, ACM, Chengdu, China, 2011, pp. 95-98.
92. J. Wang, G. Liu, H. Li, Y. Dai, Z. Wang, Detection of image region duplication forgery using model with circle block, in: International Conference on Multimedia Information Networking and Security (MINES), vol. 1, IEEE, Hubei, China, 2009, pp. 25-29.
93. J.-W. Wang, G.-J. Liu, Z. Zhang, Y.-W. Dai, and Z.-Q. Wang Robust forensics for image regional duplication and forgery based on DWT and Zernike moment Opt. Precis. Eng. 17 7 2009 1687 1693
94. V. Christlein, C. Riess, E. Angelopoulou, A study on features for the detection of copy-move forgeries, in: SICHERHEIT, vol. 2010, 2010, pp. 105-116.
95. A. Langille, M. Gong, An efficient match-based duplication detection algorithm, in: 3rd Canadian Conference on Computer and Robot Vision, IEEE, Quebec, Canada, 2006, pp. 64-64.
96. C. Barnes, E. Shechtman, D.B. Goldman, A. Finkelstein, The generalized patchmatch correspondence algorithm, in: 11th European Conference on Computer Vision (ECCV), vol. 6313, Springer, Heraklion, Crete, Greece, 2010, pp. 29-43.
97. C. Barnes, E. Shechtman, A. Finkelstein, D. Goldman, Patchmatch: a randomized correspondence algorithm for structural image editing, ACM Trans. Graph. 28 (3) (2009) 24:1-24:11.
98. D. Cozzolino, G. Poggi, L. Verdoliva, Copy-move forgery detection based on patchmatch, in: International Conference on Image Processing (ICIP), IEEE, Paris, France, 2014, pp. 5312-5316.
99. M.A. Sekeh, M.A. Maarof, M.F. Rohani, and B. Mahdian Efficient image duplicated region detection model using sequential block clustering Digit. Investig. 10 1 2013 73 84
100. M. Zandi, A. Mahmoudi-Aznaveh, A. Mansouri, Adaptive matching for copy-move forgery detection, in: International Workshop on Information Forensics and Security (WIFS), IEEE, Atlanta, GA, USA, 2014, pp. 119-124.
101. T.-T. Ng, S.-F. Chang, A model for image splicing, in: International Conference on Image Processing (ICIP), vol. 2, IEEE, Singapore, 2004, pp. 1169-1172.
102. T.-T. Ng, S.-F. Chang, Q. Sun, A Data Set of Authentic and Spliced Image Blocks, Columbia University, ADVENT Technical Report, 2004, pp. 203-204.
103. T.-T. Ng, S.-F. Chang, Q. Sun, Blind detection of photomontage using higher order statistics, in: International Symposium on Circuits and Systems (ISCAS), vol. 5, IEEE, Vancouver, BC, Canada, 2004, pp. 688-691.
104. D. Fu, Y.Q. Shi, W. Su, Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition, in: 5th International Workshop on Digital Watermarking, vol. 4283, Springer, Jeju Island, Korea, 2006, pp. 177-187.
105. X. Li, T. Jing, X. Li, Image splicing detection based on moment features and Hilbert-Huang transform, in: International Conference on Information Theory and Information Security (ICITIS), IEEE, Beijing, China, 2010, pp. 1127-1130.
106. J. Dong, W. Wang, T. Tan, Y.Q. Shi, Run-length and edge statistics based approach for image splicing detection, in: 7th International Workshop on Digital Watermarking (IWDW), vol. 5450, Springer, Berlin, Heidelberg, 2009, pp. 76-87.
107. Z. He, W. Sun, W. Lu, and H. Lu Digital image splicing detection based on approximate run length Pattern Recognit. Lett. 32 12 2011 1591 1597
108. W. Chen, Y.Q. Shi, W. Su, Image splicing detection using 2-d phase congruency and statistical moments of characteristic function, in: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 6505, Citeseer, San Jose, CA, USA, 2007.
109. Y.Q. Shi, C. Chen, W. Chen, A natural image model approach to splicing detection, in: 9th Workshop on Multimedia & Security, ACM, 2007, pp. 51-62.
110. Y.Q. Shi, C. Chen, W. Chen, A Markov process based approach to effective attacking JPEG steganography, in: 8th International Workshop on Information Hiding, vol. 4437, Springer, Alexandria, VA, USA, 2007, pp. 249-264.
111. Y.Q. Shi, G. Xuan, D. Zou, J. Gao, C. Yang, Z. Zhang, P. Chai, W. Chen, C. Chen, Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network, in: International Conference on Multimedia and Expo (ICME), IEEE, Amsterdam, Netherlands, 2005.
112. X. Zhao, S. Wang, S. Li, J. Li, A comprehensive study on third order statistical features for image splicing detection, in: 10th International Workshop on Digital Forensics and Watermarking, vol. 7128, Springer, Atlantic City, NY, USA, 2012, pp. 243-256.
113. Y. Zhang, C. Zhao, Y. Pi, S. Li, Revealing image splicing forgery using Local Binary Patterns of DCT coefficients, in: International Conference on Communications, Signal Processing, and Systems, vol. 202, Springer, Gold Coast, Australia, 2012, pp. 181-189.
114. Z. He, W. Lu, W. Sun, and J. Huang Digital image splicing detection based on Markov features in DCT and DWT domain Pattern Recognit. 45 12 2012 4292 4299
115. C. Chen, Y.Q. Shi, JPEG image steganalysis utilizing both intrablock and interblock correlations, in: International Symposium on Circuits and Systems (ISCAS), IEEE, Seattle, WA, USA, 2008, pp. 3029-3032.
116. A. Srivastava, A.B. Lee, E.P. Simoncelli, and S.-C. Zhu On advances in statistical modeling of natural images J. Math. Imaging Vis. 18 1 2003 17 33
117. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Mach. Learn. 46 1-3 2002 389 422
118. E.-S.M. El-Alfy, and M.A. Qureshi Combining spatial and DCT based markov features for enhanced blind detection of image splicing Pattern Anal. Appl. 18 3 2014 713 723
119. W. Wang, J. Dong, T. Tan, Effective image splicing detection based on image chroma, in: 16th International Conference on Image Processing (ICIP), IEEE, San Diego, CA, USA, 2009, pp. 1257-1260.
120. W. Wang, J. Dong, T. Tan, Image tampering detection based on stationary distribution of Markov chain, in: 17th International Conference on Image Processing (ICIP), IEEE, Hong Kong, 2010, pp. 2101-2104.
121. X. Zhao, J. Li, S. Li, S. Wang, Detecting digital image splicing in chroma spaces, in: Digital Watermarking, Springer, Seoul, Korea, 2011, pp. 12-22.
122. G. Muhammad, M.H. Al-Hammadi, M. Hussain, and G. Bebis Image forgery detection using steerable pyramid transform and local binary pattern Mach. Vis. Appl. 25 4 2014 985 995
123. P. Sutthiwan, Y.-Q. Shi, J. Dong, T. Tan, T.-T. Ng, New developments in color image tampering detection, in: International Symposium on Circuits and Systems (ISCAS), IEEE, Paris, France, 2010, pp. 3064-3067.
124. P. Sutthiwan, Y.Q. Shi, W. Su, T.-T. Ng, Rake transform and edge statistics for image forgery detection, in: International Conference on Multimedia and Expo (ICME), IEEE, Suntec City, Singapore, 2010, pp. 1463-1468.
125. W. Lu, W. Sun, F.-L. Chung, and H. Lu Revealing digital fakery using multiresolution decomposition and higher order statistics Eng. Appl. Artif. Intell. 24 4 2011 666 672
126. S.Q. Saleh, M. Hussain, G. Muhammad, G. Bebis, Evaluation of image forgery detection using multi-scale weber local descriptors, in: 9th International Symposium on Advances in Visual Computing (ISVC), vol. 8034, Springer, Rethymnon, Crete, Greece, 2013, pp. 416-424.
127. A. Alahmadi, M. Hussain, H. Aboalsamh, G. Muhammad, G. Bebis, et al., Splicing image forgery detection based on DCT and Local Binary Pattern, in: Global Conference on Signal and Information Processing (GlobalSIP), IEEE, Austin, TX, USA, 2013, pp. 253-256.
128. Z. Moghaddasi, H.A. Jalab, R. Md Noor, S. Aghabozorgi, Improving RLRN image splicing detection with the use of PCA and Kernel PCA, Sci. World J. (2014), 10.1155/2014/606570.
129. J. Zhang, Y. Zhao, Y. Su, A new approach merging markov and DCT features for image splicing detection, in: International Conference on Intelligent Computing and Intelligent Systems (ICIS), vol. 4, IEEE, Shanghai, China, 2009, pp. 390-394.
130. I. Amerini, R. Becarelli, R. Caldelli, A.D. Mastio, Splicing forgeries localization through the use of first digit features, in: International Workshop on Information Forensics and Security (WIFS), IEEE, Atlanta, GA, USA, 2014, pp. 143-148.
131. J. Lukáš, J. Fridrich, and M. Goljan Detecting digital image forgeries using sensor pattern noise Proc. SPIE 6072 2006 362 372
132. G. Chierchia, S. Parrilli, G. Poggi, C. Sansone, L. Verdoliva, On the influence of denoising in PRNU based forgery detection, in: 2nd ACM workshop on Multimedia in Forensics, Security and Intelligence (MiFOR), ACM, Firenze, Italy, 2010, pp. 117-122.
133. G. Chierchia, G. Poggi, C. Sansone, L. Verdoliva, PRNU-based forgery detection with regularity constraints and global optimization, in: 15th International Workshop on Multimedia Signal Processing (MMSP), IEEE, Pula, Croatia, 2013, pp. 236-241.
134. P. Kakar, N. Sudha, and W. Ser Exposing digital image forgeries by detecting discrepancies in motion blur IEEE Trans. Multimed. 13 3 2011 443 452
135. K. Bahrami, A.C. Kot, J. Fan, Splicing detection in out-of-focus blurred images, in: International Workshop on Information Forensics and Security (WIFS), IEEE, Guangzhou, China, 2013, pp. 144-149.
136. IEEE Information Forensics and Security Technical Committee (IFS-TC) (http://ifc.recod.ic.unicamp.br/fc.website/index.py?sec=0), 2013.
137. D. Cozzolino, D. Gragnaniello, L. Verdoliva, Image forgery detection through residual-based local descriptors and block-matching, in: International Conference on Image Processing (ICIP), IEEE, Paris, France, 2014, pp. 5297-5301.
138. D. Cozzolino, D. Gragnaniello, L. Verdoliva, Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques, in: International Conference on Image Processing (ICIP), IEEE, Paris, France, 2014, pp. 5302-5306.
139. L. Gaborini, P. Bestagini, S. Milani, M. Tagliasacchi, S. Tubaro, Multi-Clue image tampering localization, in: International Workshop on Information Forensics and Security (WIFS), IEEE, 2014, pp. 125-130.
140. Z. Dias, A. Rocha, and S. Goldenstein Image phylogeny by minimal spanning trees IEEE Trans. Inf. Forensics Secur. 7 2 2012 774 788
141. A.C. Popescu, and H. Farid Exposing digital forgeries by detecting traces of resampling IEEE Trans. Signal Process. 53 2 2005 758 767
142. A.C. Gallagher, Detection of linear and cubic interpolation in JPEG compressed images, in: 2nd Canadian Conference on Computer and Robot Vision, IEEE, Victoria, BC, Canada, 2005, pp. 65-72.
143. S. Prasad, K. Ramakrishnan, On resampling detection and its application to detect image tampering, in: International Conference on Multimedia and Expo (ICME), IEEE, Toronto, Canada, 2006, pp. 1325-1328.
144. M. Kirchner, Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue, in: 10th ACM Workshop on Multimedia and Security, ACM, New York, NY, USA, 2008, pp. 11-20.
145. M. Kirchner, T. Gloe, On resampling detection in re-compressed images, in: 1st International Workshop on Information Forensics and Security (WIFS), IEEE, London, UK, 2009, pp. 21-25.
146. M. Kirchner, Linear row and column predictors for the analysis of resized images, in: 12th ACM Workshop on Multimedia and Security, ACM, New York, NY, USA, 2010, pp. 13-18.
147. M. Kirchner, On the detectability of local resampling in digital images, in: Electronic Imaging, vol. 6819, International Society for Optics and Photonics, San Jose, CA, USA, 2008, pp. 68190F-7.
148. B. Mahdian, S. Saic, On periodic properties of interpolation and their application to image authentication, in: 3rd International Symposium on Information Assurance and Security (IAS), IEEE, Manchester, UK, 2007, pp. 439-446.
149. W. Weimin, W. Shuozhong, T. Zhenjun, Estimation of rescaling factor and detection of image splicing, in: 11th International Conference on Communication Technology (ICCT), IEEE, Hangzhou, China, 2008, pp. 676-679.
150. N. Dalgaard, C. Mosquera, F. Pérez-González, On the role of differentiation for resampling detection, in: 17th International Conference on Image Processing (ICIP), IEEE, Hong Kong, 2010, pp. 1753-1756.
151. G.-S. Song, Y.-I. Yun, W.-H. Lee, A new estimation approach of resampling factors using threshold-based peak detection, in: International Conference on Consumer Electronics (ICCE), IEEE, Berlin, Germany, 2011, pp. 731-732.
152. B. Mahdian, and S. Saic Blind authentication using periodic properties of interpolation IEEE Trans. Inf. Forensics Secur. 3 3 2008 529 538
153. B. Mahdian, S. Saic, Detection and description of geometrically transformed digital images, in: Media Forensics and Security, Proceedings of SPIE Electronic Imaging, vol. 7254, International Society for Optics and Photonics, San Jose, CA, USA, 2009, pp. 1-10.
154. B. Mahdian, S. Saic, A cyclostationarity analysis applied to image forensics, in: Workshop on Applications of Computer Vision (WACV), IEEE, Snowbird, Utah, 2009, pp. 1-6.
155. D. Vázquez-Padín, C. Mosquera, F. Pérez-González, Two-dimensional statistical test for the presence of almost cyclostationarity on images, in: 17th International Conference on Image Processing (ICIP), IEEE, Hong Kong, 2010, pp. 1745-1748.
156. D. Vazquez-Padin, F. Perez-Gonzalez, Prefilter design for forensic resampling estimation, in: International Workshop on Information Forensics and Security (WIFS), IEEE, Iguacu Falls, Brazil, 2011, pp. 1-6.
157. B. Mahdian, S. Saic, Detection of resampling supplemented with noise inconsistencies analysis for image forensics, in: International Conference on Computational Sciences and Its Applications (ICCSA), IEEE, Perugia, Umbria, Italy, 2008, pp. 546-556.
158. X. Feng, I.J. Cox, and G. Doërr Normalized energy density-based forensic detection of resampled images IEEE Trans. Multimed. 14 3 2012 536 545
159. S. Pfennig, M. Kirchner, Spectral methods to determine the exact scaling factor of resampled digital images, in: 5th International Symposium on Communications Control and Signal Processing (ISCCSP), IEEE, Rome, Italy, 2012, pp. 1-6.
160. H. Gou, A. Swaminathan, and M. Wu Intrinsic sensor noise features for forensic analysis on scanners and scanned images IEEE Trans. Inf. Forensics Secur. 4 3 2009 476 491
161. H. Cao, A.C. Kot, Identification of recaptured photographs on LCD screens, in: International Conference on Acoustics Speech and Signal Processing (ICASSP), IEEE, Dallas, TX, USA, 2010, pp. 1790-1793.
162. H. Muammar, P.L. Dragotti, An investigation into aliasing in images recaptured from an lcd monitor using a digital camera, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Vancouver, BC, Canada, 2013, pp. 2242-2246.
163. T. Thongkamwitoon, H. Muammar, and P.-L. Dragotti An image recapture detection algorithm based on learning dictionaries of edge profiles IEEE Trans. Inf. Forensics Secur. 10 5 2015 953 968
164. M. Visentini-Scarzanella, P.L. Dragotti, Modelling radial distortion chains for video recapture detection, in: 15th International Workshop on Multimedia Signal Processing (MMSP), IEEE, Pula, Croatia, 2013, pp. 412-417.
165. N. Khanna, G.T.-C. Chiu, J.P. Allebach, E.J. Delp, Forensic techniques for classifying scanner, computer generated and digital camera images, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Las Vegas, NV, USA, 2008, pp. 1653-1656.
166. D.-T. Dang-Nguyen, G. Boato, F.G. De Natale, Discrimination between computer generated and natural human faces based on asymmetry information, in: 20th European Signal Processing Conference (EUSIPCO), IEEE, Bucharest, Romania, 2012, pp. 1234-1238.
167. F. Peng, J.-t. Li, and M. Long Identification of natural images and computer-generated graphics based on statistical and textural features J. Forensic Sci. 60 2 2015 435 443
168. A. Criminisi, P. Pérez, and K. Toyama Region filling and object removal by exemplar-based image inpainting IEEE Trans. Image Process. 13 9 2004 1200 1212
169. S. Das, G.D. Shreyas, and L.D. Devan Blind detection method for video inpainting forgery Int. J. Comput. Appl. 60 11 2012 33 37
170. I.-C. Chang, J.C. Yu, and C.-C. Chang A forgery detection algorithm for exemplar-based inpainting images using multi-region relation Image Vis. Comput. 31 1 2013 57 71
171. Z. Liang, G. Yang, X. Ding, and L. Li An efficient forgery detection algorithm for object removal by exemplar-based image inpainting J. Visual Commun. Image Represent 30 2015 75 85
172. Y.Q. Zhao, M. Liao, F.Y. Shih, and Y.Q. Shi Tampered region detection of inpainting JPEG images Optik-Int. J. Light Electron Opt. 124 16 2013 2487 2492
173. D.T. Trung, A. Beghdadi, M.-C. Larabi, Blind inpainting forgery detection, in: Global Conference on Signal and Information Processing (GlobalSIP), IEEE, Atlanta, GA, USA, 2014, pp. 1019-1023.
174. H. Gunes, and M. Piccardi Assessing facial beauty through proportion analysis by image processing and supervised learning Int. J. Human-Comput. Stud. 64 12 2006 1184 1199
175. A. Dantcheva, J. Dugelay, Female facial aesthetics based on soft biometrics and photo-quality, in: International Conference for Multimedia and Expo (ICME), IEEE, Barcelona, Spain, 2011.
176. X.-F. Li, X.-J. Shen, and H.-P. Chen Blind identification algorithm for retouched images based on Bi-Laplacian J. Comput. Appl. 31 1 2011 239 242
177. E. Kee, and H. Farid A perceptual metric for photo retouching Proc. Natl. Acad. Sci. 108 50 2011 19907 19912
178. D. Xie, L. Liang, L. Jin, J. Xu, M. Li, Scut-fbp: a benchmark dataset for facial beauty perception (http://www.hcii-lab.net/data/SCUT-FBP/), Accessed: 2015-06-14, 2015.
179. M. Kirchner, J. Fridrich, On detection of median filtering in digital images, in: IS&T/SPIE Electronic Imaging, vol. 7541, International Society for Optics and Photonics, San Jose, CA, USA, 2010, pp. 754110-754110-12.
180. T. Pevny, P. Bas, and J. Fridrich Steganalysis by subtractive pixel adjacency matrix IEEE Trans. Inf. Forensics Secur. 5 2 2010 215 224
181. G. Cao, Y. Zhao, R. Ni, L. Yu, H. Tian, Forensic detection of median filtering in digital images, in: International Conference on Multimedia and Expo (ICME), IEEE, Suntec City, Singapore, 2010, pp. 89-94.
182. H.-D. Yuan Blind forensics of median filtering in digital images IEEE Trans. Inf. Forensics Secur. 6 4 2011 1335 1345
183. X. Kang, M.C. Stamm, A. Peng, and K.R. Liu Robust median filtering forensics using an autoregressive model IEEE Trans. Inf. Forensics Secur. 8 9 2013 1456 1468
184. M. Stamm, K.R. Liu, Blind forensics of contrast enhancement in digital images, in: 15th International Conference on Image Processing (ICIP), IEEE, San Diego, CA, USA, 2008, pp. 3112-3115.
185. M.C. Stamm, K.R. Liu, Forensic estimation and reconstruction of a contrast enhancement mapping, in: International Conference on Acoustics Speech and Signal Processing (ICASSP), IEEE, Dallas, TX, USA, 2010, pp. 1698-1701.
186. M.C. Stamm, and K.R. Liu Forensic detection of image manipulation using statistical intrinsic fingerprints IEEE Trans. Inf. Forensics Secur. 5 3 2010 492 506
187. S.D. Mahalakshmi, K. Vijayalakshmi, and S. Priyadharsini Digital image forgery detection and estimation by exploring basic image manipulations Digit. Investig. 8 3 2012 215 225
188. G. Cao, Y. Zhao, R. Ni, and X. Li Contrast enhancement-based forensics in digital images IEEE Trans. Inf. Forensics Secur. 9 3 2014 515 525
189. H.-D. Yuan, Identification of global histogram equalization by modeling gray-level cumulative distribution, in: China Summit & International Conference on Signal and Information Processing (ChinaSIP), IEEE, Beijing, China, 2013, pp. 645-649.
190. G. Cao, Y. Zhao, R. Ni, Forensic estimation of gamma correction in digital images, in: 17th International Conference on Image Processing (ICIP), IEEE, Hong Kong, 2010, pp. 2097-2100.
191. G. Cao, Y. Zhao, R. Ni, Detection of image sharpening based on histogram aberration and ringing artifacts, in: International Conference on Multimedia and Expo (ICME), IEEE, New York, NY, USA, 2009, pp. 1026-1029.
192. G. Cao, Y. Zhao, R. Ni, and A.C. Kot Unsharp masking sharpening detection via overshoot artifacts analysis IEEE Signal Process. Lett. 18 10 2011 603 606
193. F. Ding, G. Zhu, Y.Q. Shi, A novel method for detecting image sharpening based on local binary pattern, in: Digital-Forensics and Watermarking, Springer, Auckland, New Zealand, 2014, pp. 180-191.
194. F. Ding, G. Zhu, J. Yang, J. Xie, and Y.-Q. Shi Edge perpendicular binary coding for USM sharpening detection IEEE Signal Process. Lett. 22 2015 327 331
195. G. Cao, Y. Zhao, R. Ni, B. Ou, and Y. Wang Forensic detection of noise addition in digital images J. Electron. Imaging 23 2 2014 023004
196. M.C. Stamm, X. Chu, K.R. Liu, Forensically determining the order of signal processing operations, in: International Workshop on Information Forensics and Security (WIFS), IEEE, Guangzhou, China, 2013, pp. 162-167.
197. I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra A SIFT-based forensic method for copy-move attack detection and transformation recovery IEEE Trans. Inf. Forensics Secur. 6 3 PART 2 2011 1099 1110
198. Y.-F. Hsu, S.-F. Chang, Detecting image splicing using geometry invariants and camera characteristics consistency, in: International Conference on Multimedia and Expo (ICME), 2006, pp. 549-552.
199. R. Böhme, M. Kirchner, Counter-forensics: attacking image forensics, in: Digital Image Forensics, Springer, New York, 2013, pp. 327-366.
200. M. Kirchner, Notes on digital image forensics and counter-forensics (Ph.D. thesis), Dartmouth College, 2012.
201. G. Cao, Y. Zhao, R. Ni, H. Tian, Anti-forensics of contrast enhancement in digital images, in: 12th ACM Workshop on Multimedia and Security, ACM, New York, NY, USA, 2010, pp. 25-34.
202. C.-W. Kwok, O.C. Au, S.-H. Chui, Alternative anti-forensics method for contrast enhancement, in: Digital Forensics and Watermarking, vol. 7128, Springer, Atlantic City, NY, USA, 2012, pp. 398-410.
203. L. Laijie, Y. Gaobo, and X. Ming Anti-forensics for unsharp masking sharpening in digital images Int. J. Digit. Crime Forensics 5 3 2013 53 65
204. Z.-H. Wu, M.C. Stamm, K.R. Liu, Anti-forensics of median filtering, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Vancouver, BC, Canada, 2013, pp. 3043-3047.
205. M.C. Stamm, and K. Liu Anti-forensics of digital image compression IEEE Trans. Inf. Forensics Secur. 6 3 2011 1050 1065
206. M.C. Stamm, S.K. Tjoa, W.S. Lin, K. Liu, Undetectable image tampering through JPEG compression anti-forensics, in: 17th International Conference on Image Processing (ICIP), IEEE, Hong Kong, 2010, pp. 2109-2112.
207. W. Fan, K. Wang, F. Cayre, Z. Xiong, A variational approach to JPEG anti-forensics, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Vancouver, BC, Canada, 2013, pp. 3058-3062.
208. W. Fan, K. Wang, F. Cayre, and Z. Xiong JPEG anti-forensics with improved tradeoff between forensic undetectability and image quality IEEE Trans. Inf. Forensics Secur. 9 8 2014 1211 1226
209. H. Li, W. Luo, J. Huang, Countering anti-JPEG compression forensics, in: 19th International Conference on Image Processing (ICIP), IEEE, Orlando, FL, USA, 2012, pp. 241-244.
210. G. Valenzise, M. Tagliasacchi, S. Tubaro, The cost of JPEG compression anti-forensics, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Prague, Czech Republic, 2011, pp. 1884-1887.
211. M.C. Stamm, K. Liu, Wavelet-based image compression anti-forensics, in: 17th International Conference on Image Processing (ICIP), IEEE, Hong Kong, 2010, pp. 1737-1740.
212. M. Kirchner, and R. Böhme Hiding traces of resampling in digital images IEEE Trans. Inf. Forensics Secur. 3 4 2008 582 592
213. T. Gloe, M. Kirchner, A. Winkler, R. Böhme, Can we trust digital image forensics? in: 15th International Conference on Multimedia, ACM, Augsburg, Bavaria, Germany, 2007, pp. 78-86.
214. R. Böhme, M. Kirchner, Synthesis of color filter array pattern in digital images, Proc. SPIE, Media Forensics Secur. XI, 7254 (2009) 18-22.
215. A. Rocha, W. Scheirer, T. Boult, S. Goldenstein, Vision of the unseen: current trends and challenges in digital image and video forensics, ACM Comput. Surv. 43 (4) (2011) 26:1-26:42.
216. G. Cao, Y. Zhao, R. Ni, H. Tian, Anti-forensics of contrast enhancement in digital images, in: 12th ACM Workshop on Multimedia and Security, ACM, New York, NY, USA, 2010, pp. 25-34.
217. M. Fontani, A. Bonchi, A. Piva, M. Barni, Countering anti-forensics by means of data fusion, in: IS&T/SPIE Electronic Imaging, vol. 9028, International Society for Optics and Photonics, San Francisco, CA, USA, 2014, pp. 90280Z-90280Z-15.
218. G. Valenzise, V. Nobile, M. Tagliasacchi, S. Tubaro, Countering JPEG anti-forensics, in: 18th International Conference on Image Processing (ICIP), IEEE, Brussels, Belgium, 2011, pp. 1949-1952.
219. G. Valenzise, M. Tagliasacchi, and S. Tubaro Revealing the traces of JPEG compression anti-forensics IEEE Trans. Inf. Forensics Secur. 8 2 2013 335 349
220. S. Lai, R. Böhme, Countering counter-forensics: the case of JPEG compression, in: 13th International Conference on Information Hiding, vol. 6958, Springer, Prague, Czech Republic, 2011, pp. 285-298.
221. G. Cao, Y. Zhao, and R. Ni Forensic identification of resampling operators: a semi non-intrusive approach Forensic Sci. Int. 216 1 2012 29 36
222. H. Zeng, T. Qin, X. Kang, L. Liu, Countering anti-forensics of median filtering, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Florence, Italy, 2014, pp. 2704-2708.
223. A. De Rosa, M. Fontani, M. Massai, A. Piva, and M. Barni Second-order statistics analysis to cope with contrast enhancement counter-forensics IEEE Signal Process. Lett. 22 8 2015 1132 1136
224. A. Costanzo, I. Amerini, R. Caldelli, and M. Barni Forensic analysis of SIFT keypoint removal and injection IEEE Trans. Inf. Forensics Secur. 9 9 2014 1450 1464
225. I. Amerini, F. Battisti, R. Caldelli, M. Carli, A. Costanzo, Exploiting perceptual quality issues in countering sift-based forensic methods, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Florence, Italy, 2014, pp. 2664-2668.
226. F. Battisti, M. Carli, A. Neri, Image forgery detection by means of no-reference quality metrics, in: IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics, Burlingame, CA, USA, 2012, p. 83030K.
227. Trung Thanh Dang, Azeddine Beghdadi, and M-C Larabi A perceptual image completion approach based on a hierarchical optimization scheme Signal Processing 103 2014 127 141
228. Trung Thanh Dang, Azeddine Beghdadi, and Mohamed-Chaker Larabi Perceptual quality assessment for color image inpainting 20th International Conference on Image Processing (ICIP) 2013 IEEE Melbourne, Australia 398 402
229. Mohamed Deriche An Image Quality Index Based on Mutual Information and Neural Networks Arabian Journal for Science and Engineering 39 3 2014 1983 1993