A copy-move image forgery detection technique based on Gaussian-Hermite moments

Images are one of the most prominently used digital information sharing medium now a days. Due to availability of state-of-the-art image editing tools it has become very easy to forge an image. Among various types of image forgeries, copy-move (region-duplication) forgery cases are emerging very frequently. In copy-move image forgery one or more regions of an image are replicated within the same image. In this paper, a new robust copy-move image forgery detection technique is proposed using Gaussian-Hermite Moments (GHM). The proposed technique divides the input image into overlapping blocks of fixed size and then the Gaussian-Hermite moments are extracted for each block. The matching of similar blocks is done by sorting all the features lexicographically. The experimental results show that the proposed technique can locate the copy-move forged regions in a forged image very accurately. The proposed technique shows promising results in the presence of various post-processing operations scaling, blurring, color reduction, adjustment of brightness, rotation, and JPEG compression.

[1]  M. Ulutas,et al.  A new copy move forgery detection technique with automatic threshold determination , 2016 .

[2]  Anderson Rocha,et al.  Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes , 2015, J. Vis. Commun. Image Represent..

[3]  Paul L. Rosin,et al.  Combining cellular automata and local binary patterns for copy-move forgery detection , 2015, Multimedia Tools and Applications.

[4]  Muhammad Ghulam,et al.  Passive detection of image forgery using DCT and local binary pattern , 2016, Signal, Image and Video Processing.

[5]  M. Wilscy,et al.  Image forgery detection using region - based Rotation invariant Co-occurrences among adjacent LBPs , 2018, J. Intell. Fuzzy Syst..

[6]  Alberto Del Bimbo,et al.  Ieee Transactions on Information Forensics and Security 1 a Sift-based Forensic Method for Copy-move Attack Detection and Transformation Recovery , 2022 .

[7]  Luo Wei,et al.  Robust Detection of Region-Duplication Forgery in Digital Image , 2007 .

[8]  M. Ulutas,et al.  Rotation invariant copy move forgery detection method , 2015, 2015 9th International Conference on Electrical and Electronics Engineering (ELECO).

[9]  Chi-Man Pun,et al.  Fast copy-move forgery detection using local bidirectional coherency error refinement , 2018, Pattern Recognit..

[10]  Jie Zhao,et al.  Passive Forensics for Region Duplication Image Forgery Based on Harris Feature Points and Local Binary Patterns , 2013 .

[11]  R. Menaka,et al.  Computer-aided detection and characterization of stroke lesion – a short review on the current state-of-the art methods , 2018 .

[12]  Xingming Sun,et al.  An Efficient Forensic Method for Copy – move Forgery Detection Based on DWT-FWHT , 2013 .

[13]  N. Ohnishi,et al.  Exploring duplicated regions in natural images. , 2010, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[14]  Jun Shen.  Orthogonal Gaussian-Hermite moments for image characterization , 1997, Other Conferences.

[15]  Davide Cozzolino,et al.  Copy-move forgery detection based on PatchMatch , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[16]  Chi-Man Pun,et al.  Multi-Level Dense Descriptor and Hierarchical Feature Matching for Copy-Move Forgery Detection , 2016, Inf. Sci..

[17]  Ahmad Mahmoudi Aznaveh,et al.  Iterative Copy-Move Forgery Detection Based on a New Interest Point Detector , 2016, IEEE Transactions on Information Forensics and Security.

[18]  Edoardo Ardizzone,et al.  > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < , 2007 .

[19]  Xingming Sun,et al.  Segmentation-Based Image Copy-Move Forgery Detection Scheme , 2015, IEEE Transactions on Information Forensics and Security.

[20]  Alin C. Popescu,et al.  Exposing Digital Forgeries by Detecting Duplicated Image Regions Exposing Digital Forgeries by Detecting Duplicated Image Regions , 2004 .

[21]  Khalid M. Hosny,et al.  Copy-move forgery detection of duplicated objects using accurate PCET moments and morphological operators , 2018 .

[22]  Jessica Fridrich,et al.  Detection of Copy-Move Forgery in Digital Images , 2004 .

[23]  Vikas Maheshkar,et al.  An integrated method of copy-move and splicing for image forgery detection , 2018, Multimedia Tools and Applications.

[24]  I. Elishakoff,et al.  Antioptimization of earthquake exitation and response , 1998 .

[25]  Renlong Pan,et al.  License Plate Character Recognition Based on Gaussian-Hermite Moments , 2010, 2010 Second International Workshop on Education Technology and Computer Science.

[26]  Xunyu Pan,et al.  Region Duplication Detection Using Image Feature Matching , 2010, IEEE Transactions on Information Forensics and Security.

[27]  S. P. Ghrera,et al.  Pixel-Based Image Forgery Detection: A Review , 2014 .

[28]  Amel Benazza-Benyahia,et al.  Efficient transform-based texture image retrieval techniques under quantization effects , 2016, Multimedia Tools and Applications.

[29]  Arsalane Zarghili,et al.  3D Face Recognition using Gaussian Hermite Moments , 2012 .

[30]  S. Sons.  Detection of Region Duplication Forgery in Digital Images Using SURF , 2011 .

[31]  Chi-Man Pun,et al.  Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching , 2015, IEEE Transactions on Information Forensics and Security.

[32]  Xu Bo,et al.  Image Copy-Move Forgery Detection Based on SURF , 2010, 2010 International Conference on Multimedia Information Networking and Security.

[33]  Chien-Ping Chang,et al.  Detection of copy-move image forgery using histogram of orientated gradients , 2015, Inf. Sci..

[34]  Jun Shen,et al.  Moving object detection using orthogonal Gaussian-Hermite moments , 2004, IS&T/SPIE Electronic Imaging.

[35]  Mo Dai,et al.  Rotation and translation invariants of Gaussian-Hermite moments , 2011, Pattern Recognit. Lett..

[36]  Alberto Del Bimbo,et al.  Copy-move forgery detection and localization by means of robust clustering with J-Linkage , 2013, Signal Process. Image Commun..

[37]  Paul L. Rosin,et al.  Detection of duplicated image regions using cellular automata , 2014, IWSSIP 2014 Proceedings.

[38]  Zahid Mehmood,et al.  A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform , 2018, J. Vis. Commun. Image Represent..

[39]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[40]  Xizhao Wang,et al.  Segmenting time series with connected lines under maximum error bound , 2016, Inf. Sci..

[41]  Hong-Ying Yang,et al.  Robust copy-move forgery detection based on multi-granularity Superpixels matching , 2017, Multimedia Tools and Applications.

[42]  Davide Cozzolino,et al.  Efficient Dense-Field Copy–Move Forgery Detection , 2015, IEEE Transactions on Information Forensics and Security.

[43]  Asoke K. Nandi,et al.  Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics , 2011, Signal Process..

[44]  Babu M. Mehtre,et al.  Copy-move tampering detection using affine transformation property preservation on clustered keypoints , 2018, Signal Image Video Process..

[45]  Chi-Man Pun,et al.  A two-stage localization for copy-move forgery detection , 2018, Inf. Sci..

[46]  Vipin Tyagi,et al.  Image Forgery Detection: Survey and Future Directions , 2019, Data, Engineering and Applications.

[47]  Xianfeng Zhao,et al.  Copy-move forgery detection based on convolutional kernel network , 2017, Multimedia Tools and Applications.

[48]  Sonja Grgic,et al.  CoMoFoD — New database for copy-move forgery detection , 2013, Proceedings ELMAR-2013.

[49]  Heung-Kyu Lee,et al.  Rotation Invariant Localization of Duplicated Image Regions Based on Zernike Moments , 2013, IEEE Transactions on Information Forensics and Security.

[50]  Vipin Tyagi,et al.  Understanding Digital Image Processing , 2018 .

[51]  YoufuWu.  Properties of Orthogonal Gaussian-Hermite Moments and Their Applications , 2005 .

[52]  Yanfen Gan,et al.  Detection of copy–move forgery using discrete analytical Fourier–Mellin transform , 2016 .

[53]  Christian Riess,et al.  Ieee Transactions on Information Forensics and Security an Evaluation of Popular Copy-move Forgery Detection Approaches , 2022 .

[54]  Ye Zhu,et al.  Copy-move forgery detection based on scaled ORB , 2015, Multimedia Tools and Applications.

[55]  Jichang Guo,et al.  Passive forensics for copy-move image forgery using a method based on DCT and SVD. , 2013, Forensic science international.

[56]  Wei Lu,et al.  Region duplication detection based on Harris corner points and step sector statistics , 2013, J. Vis. Commun. Image Represent..

[57]  Hong-Ying Yang,et al.  A new keypoint-based copy-move forgery detection for small smooth regions , 2017, Multimedia Tools and Applications.

[58]  Hong-Ying Yang,et al.  Robust copy–move forgery detection using quaternion exponent moments , 2018, Pattern Analysis and Applications.

[59]  Jan Flusser,et al.  Scale invariants from Gaussian-Hermite moments , 2017, Signal Process..