Neeru JINDAL, Amanjot Kaur LAMBA, Sanjay SHARMA

Digital image copy-move forgery detection based on discrete fractional wavelet transform

With the advancement of sophisticated cameras and image editing software tools, digital image tampering techniques are frequently used without leaving visual cues behind. Digital image copy-move forgery is a kind of image manipulation that involves copying and pasting of a certain section (or sections) within the same digital image. Generally, this is done with false intentions of hiding important information or providing false information in an image. In view of this, the focus of the present paper is to propose a discrete fractional wavelet transform-based scheme for identification of duplicated regions in the image. The test image is split into overlapping image blocks with fixed dimensions. Then, on each image block, discrete fractional wavelet transform is employed for the extraction of their features. All the feature vectors are systematized in lexicographical manner followed by the block matching and block filtering steps to obtain the replicated blocks, if any. The proposed method can detect single and multiple duplicated regions successfully. The results are compared to existing techniques based on precision and recall parameters. Simulation results show that the proposed forgery detection scheme can detect tampering areas even in the presence of distortions due to Gaussian blurring and JPEG compression.

PDF

___

Chang IC, Yu JC, Chang CC. A forgery detection algorithm for exemplar-based inpainting images using multi-region relation. Image Vision Comput 2013; 31: 57-71.
Ryu SJ, Lee MJ, Lee HK. Detection of copy-rotate-move forgery using Zernike moments. In: International Workshop on Information Hiding; 2010; Berlin, Germany.
Cao Y, Gao T, Fan L, Yang Q. A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 2012; 214: 33-43.
Zhang J, Feng Z, Su Y. A new approach for detecting copy-move forgery in digital images. In: IEEE 2008 International Conference on Communication Systems; 19–21 November 2008; Guangzhou, China. New York, NY, USA: IEEE. pp. 362-366.
Khan S, Kulkarni A. Reduced time complexity for detection of copy-move forgery using discrete wavelet transform. Int J Comput Appl 2010; 6: 31-36.
Muhammad G, Al-Hammadi MM, Hussain M, Bebis G. Image forgery detection using steerable pyramid transform and local binary pattern. Mach Vision Appl 2014; 25: 985-995.
Rao Y, Ni J. A deep learning approach to detection of splicing and copy-move forgeries in images. In: International Workshop on Information Forensics and Security; 2016; Abu Dhabi, UAE.
Mangat SS, Kaur H. A review of literature on copy-move forgery detection techniques. Int J Comput Sci Inf Tech Secur 2016; 6: 482-486.
Fadl SM, Semary NA, Hadhoud MM. Fan search for image copy-move forgery detection. In: Springer 2014 International Conference on Advanced Machine Learning Technologies and Applications; 28–30 November 2014; Cairo, Egypt. Berlin, Germany: Springer. pp. 177-186.
Singh VK, Tripathi RC. Fast and efficient region duplication detection in digital images using sub-blocking method. Int J Adv Sci Tech 2011; 35: 93-102.
Huang Y, Lu W, Sun W, Long D. Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 2011; 206: 178-184.
Ng TT. Columbia Image Splicing Detection Evaluation Dataset. New York, NY, USA: Columbia University, 2004.
Mohsen Z, Ahmad MA, Mansouri A. Adaptive matching for copy-move forgery detection. In: International Workshop on Information Forensics and Security; 2014; Atlanta, GA, USA.
Wang Y, Gurule K, Wise J, Zheng J. Wavelet based region duplication forgery detection. In: IEEE 2012 Ninth International Conference on Information Technology-New Generations; 16–18 April 2012, Las Vegas, NV, USA. New York, NY, USA: IEEE. pp. 30-35.
Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E. An evaluation of popular copy-move forgery detection approaches. IEEE T Inf Foren Sec 2012; 7: 1841-1854.
Lynch G, Shih FY, Liao HYM. An efficient expanding block algorithm for image copy-move forgery detection. Inform Sciences 2013; 239: 253-265.
Diane WNN, Xingming S, Moise FK. A survey of partition-based techniques for copy-move forgery detection. Scientific World J 2014; 2014: 975456.
Gan Y, Zhong J. Image copy-move forgery blind detection algorithm based on the normalized histogram multifeature vectors. J Soft Eng 2015; 9: 254-264.
Bhatnagar G, Wu QMJ, Raman B. Discrete fractional wavelet transform and its application to multiple encryption. Inform Sciences 2013; 223: 297-316.
Dixit R, Naskar R. Review, analysis and parameterization of techniques for copy-move forgery detection in digital images. IET Image Processing 2017; 11: 746-759.
Zhong J, Gan Y, Young J, Huang L, Lin P. A new block-based method for copy move forgery detection under image geometric transforms. Multimed Tools Appl 2017; 76: 14887-14903.
Asghar K, Habib Z, Hussain M. Copy-move and splicing image forgery detection and localization techniques: a review. Aus J Forensic Sci 2017; 49: 281-307.
Liu G, Wang J, Lian S, Wang Z. A passive image authentication scheme for detecting region-duplication forgery with rotation. J Netw Comput Appl 2011; 34: 1557-1565.
Ouyang J, Liu Y, Shu H. Robust hashing for image authentication using SIFT feature and quaternion Zernike moments. Multimed Tools Appl 2015; 76: 2609-2626.
Mahdian B, Saic S. Detection of copy-move forgery using a method based on blur moment invariants. Forensic Sci Int 2007; 171: 180-189.
Qiao M, Sung A, Liu Q, Ribeiro BM. A novel approach for detection of copy-move forgery. In: IARIA International Conference on Advanced Engineering Computing and Applications in Sciences; 20–25 November 2011; Lisbon, Portugal. Wilmington, DE, USA: IARIA. pp. 44-47.
Bayram S, Sencar HT, Memon N. An efficient robust method for detecting copy-move forgery. In: IEEE 2009 International Conference on Acoustics, Speech, and Signal Processing; 19–24 April 2009; Taipei, Taiwan. New York, NY, USA: IEEE. pp. 1053-1056.
Popescu AC, Farid H. Exposing Digital Forgeries by Detecting Duplicated Image Regions. Technical Report TR2004-515. Hanover, NH, USA: Dartmouth College, 2004.
Bhosale S, Thube G, Jangam P, Borse R. Employing SVD and wavelets for digital image forensics and tampering detection. In: IEEE 2012 International Conference on Advances in Mobile Network, Communication and Its Applications; 1–2 August 2012; Bangalore, India. New York, NY, USA: IEEE. pp. 135-138.
Fridrich J, Soukal D, Lukas J. Detection of copy-move forgery in digital images. In: Digital Forensic Research Workshop; 2003; Cleveland, OH, USA.
Huang Y, Lu W, Sun W, Long D. Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 2011; 206: 178-184.
Al-Qershi OM, Khoo BE. Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Sci Int 2013; 231: 284-295.
Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E. An evaluation of popular copy-move forgery detection approaches. IEEE T Inf Foren Sec 2012; 7: 1841-1854.
Qureshi MA, Deriche M. A bibliography of pixel-based blind image forgery detection techniques. Signal ProcessImage 2015; 39: 46-74.
Qazi T, Hayat K, Khan SU, Madani SA, Khan IA, Kolodziej J, Li H, Lin W, Yow KC, Xu CZ. Survey on blind image forgery detection. IET Image Process 2013; 7: 660-670.
Bayram S, Sencar HT, Memon N. A survey of copy-move forgery detection techniques. In: Western Network York Image Processing Workshop; 2008.