A fast and effective digital image copy move forgery detection with binarized SIFT

With the widespread use of digital images almost every field so authentication of them has become increasingly important. So researchers proposed various methods to cope with this issue recently. We proposed a new fast and effective method to cope with the digital image copy move forgery. In this method the keypoints and their descriptors are extracted from the input image by using Scale Invariant Feature Transform (SIFT) algorithm. For faster matching, the descriptor vectors are binarized 128 dimension to 128 bit and their hashing value are calculated. Then forged regions are detected by matching binarized descriptors with the same hash value. According to experimental results, the method detects forged regions on the images even if the forged image has undergone some post processing and preprocessing attacks.

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

[2]  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 .

[3]  Nasir D. Memon,et al.  An efficient and robust method for detecting copy-move forgery , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Shang-Lin Hsieh,et al.  Using binarization and hashing for efficient SIFT matching , 2015, J. Vis. Commun. Image Represent..

[5]  John F. Roddick,et al.  An Efficient Scheme for Detecting Copy-move Forged Images by Local Binary Patterns , 2013, J. Inf. Hiding Multim. Signal Process..

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

[7]  Copy-Move Attack Forgery Detection by Using SIFT , 2013 .

[8]  Hulya Dogan,et al.  Image forgery detection based on Colour SIFT , 2015, 2015 23nd Signal Processing and Communications Applications Conference (SIU).

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

[10]  Haizhen He,et al.  Exposing Copy-move Forgeries Based on a Dimension-reduced Sift Method , 2013 .

[11]  Qi Han,et al.  PCET based copy-move forgery detection in images under geometric transforms , 2015, Multimedia Tools and Applications.

[12]  Yu Zhang,et al.  Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

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

[14]  Mohammad Farukh Hashmi,et al.  Copy move forgery detection using DWT and SIFT features , 2013, 2013 13th International Conference on Intellient Systems Design and Applications.

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

[16]  Na Fan,et al.  Digital image modification detection using color information and its histograms. , 2016, Forensic science international.

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

[18]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[19]  Xu Bo,et al.  Image Copy-Move Forgery Detection Based on SURF , 2010, 2010 International Conference on Multimedia Information Networking 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]  Mauro Barni,et al.  Counter-forensics of SIFT-based copy-move detection by means of keypoint classification , 2013, EURASIP J. Image Video Process..