Passive Forensics for Region Duplication Image Forgery Based on Harris Feature Points and Local Binary Patterns

Nowadays the demand for identifying the authenticity of an image is much increased since advanced image editing software packages are widely used. Region duplication forgery is one of the most common and immediate tampering attacks which are frequently used. Several methods to expose this forgery have been developed to detect and locate the tampered region, while most methods do fail when the duplicated region undergoes rotation or flipping before being pasted. In this paper, an efficient method based on Harris feature points and local binary patterns is proposed. First, the image is filtered with a pixelwise adaptive Wiener method, and then dense Harris feature points are employed in order to obtain a sufficient number of feature points with approximately uniform distribution. Feature vectors for a circle patch around each feature point are extracted using local binary pattern operators, and the similar Harris points are matched based on their representation feature vectors using the BBF algorithm. Finally, RANSAC algorithm is employed to eliminate the possible erroneous matches. Experiment results demonstrate that the proposed method can effectively detect region duplication forgery, even when an image was distorted by rotation, flipping, blurring, AWGN, JPEG compression, and their mixed operations, especially resistant to the forgery with the flat area of little visual structures.

[1]  Wei-Tsong Lee,et al.  Free View Point Real-Time Monitor System Based on Harris-SURF , 2013 .

[2]  David G. Lowe,et al.  Shape indexing using approximate nearest-neighbour search in high-dimensional spaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Tiegang Gao,et al.  A robust detection algorithm for copy-move forgery in digital images. , 2012, Forensic science international.

[4]  Dan Tu,et al.  An image quality metric based on the Harris corner , 2012, 2012 IEEE 11th International Conference on Signal Processing.

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

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

[7]  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 .

[8]  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.

[9]  Jingyu Yang,et al.  Remote sensing image matching algorithm based on Harris and SIFT transform , 2012 .

[10]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  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.

[12]  Liming Chen,et al.  Image region description using orthogonal combination of local binary patterns enhanced with color information , 2013, Pattern Recognit..

[13]  Xuejin Gao,et al.  An Indoor Mobile Visual Localization Algorithm Based On Harris-Sift , 2012, Intell. Autom. Soft Comput..

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

[15]  XiaoBing Kang,et al.  Identifying Tampered Regions Using Singular Value Decomposition in Digital Image Forensics , 2008, 2008 International Conference on Computer Science and Software Engineering.

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

[17]  Girija Chetty,et al.  A novel image watermarking scheme using Extreme Learning Machine , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

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

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

[20]  Yue Chen,et al.  A novel chaos-based fragile watermarking for image tampering detection and self-recovery , 2013, Signal Process. Image Commun..

[21]  Qiong Wu,et al.  A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD , 2007, 2007 IEEE International Conference on Multimedia and Expo.

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

[23]  Wei Sun,et al.  Improved DCT-based detection of copy-move forgery in images. , 2011, Forensic science international.

[24]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

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