An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features

The popularity of image editing software has made it increasingly easy to alter the content of images. These alterations threaten the authenticity and integrity of images, causing misjudgments and possibly even affecting social stability. The copy-move technique is one of the most commonly used approaches for manipulating images. As a defense, the image forensics technique has become popular for judging whether a picture has been tampered with via copy-move, splicing, or other forgery techniques. In this paper, a scheme based on accelerated-KAZE (A-KAZE) and speeded-up robust features (SURF) is proposed for image copy-move forgery detection (CMFD). It is difficult for most keypoint-based CMFD methods to obtain sufficient points in smooth regions. To remedy this defect, the response thresholds for the A-KAZE and SURF feature detection stages are set to small values in the proposed method. In addition, a new correlation coefficient map is presented, in which the duplicated regions are demarcated, combining filtering and mathematical morphology operations. Numerous experiments are conducted to demonstrate the effectiveness of the proposed method in searching for duplicated regions and its robustness against distortions and post-processing techniques, such as noise addition, rotation, scaling, image blurring, joint photographic expert group (JPEG) compression, and hybrid image manipulation. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested CMFD methods.

[1]  Muhammad Ghulam,et al.  Accurate and robust localization of duplicated region in copy–move image forgery , 2014, Machine Vision and Applications.

[2]  Noura A. Semary,et al.  Robust Copy-Move forgery revealing in digital images using polar coordinate system , 2017, Neurocomputing.

[3]  Dragica Radosav,et al.  Deep Learning and Medical Diagnosis: A Review of Literature , 2018, Multimodal Technol. Interact..

[4]  Yanfen Gan,et al.  A new block-based method for copy move forgery detection under image geometric transforms , 2017, Multimedia Tools and Applications.

[5]  Christian Heipke,et al.  Probabilistic multi-person localisation and tracking in image sequences , 2017 .

[6]  Adrien Bartoli,et al.  KAZE Features , 2012, ECCV.

[7]  Ainuddin Wahid Abdul Wahab,et al.  Image Region Duplication Forgery Detection Based on Angular Radial Partitioning and Harris Key-Points , 2016, Symmetry.

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

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

[10]  Zheng Li,et al.  Copy-move Forgery Detection Robust to Various Transformation and Degradation Attacks , 2018, KSII Trans. Internet Inf. Syst..

[11]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[12]  Qing Wang,et al.  Double JPEG compression forensics based on a convolutional neural network , 2016, EURASIP J. Inf. Secur..

[13]  Biao Li,et al.  Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images , 2018, Sensors.

[14]  Adrien Bartoli,et al.  Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces , 2013, BMVC.

[15]  Farhad Samadzadegan,et al.  Determination of Optimum Classifier and Feature Subset in Hyperspectral Images based on Ant Colony System , 2012 .

[16]  Mohammad Ziaullah,et al.  Image feature based authentication and digital signature for wireless data transmission , 2016, 2016 International Conference on Computer Communication and Informatics (ICCCI).

[17]  Qi Han,et al.  Feature point-based copy-move forgery detection: covering the non-textured areas , 2014, Multimedia Tools and Applications.

[18]  Feng Cheng,et al.  Recent Advances in Passive Digital Image Security Forensics: A Brief Review , 2018 .

[19]  Xin Yang,et al.  LDB: An ultra-fast feature for scalable Augmented Reality on mobile devices , 2012, 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).

[20]  Toni F. Schenk,et al.  Feature-based registration of historical aerial images by Area Minimization , 2016 .

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

[22]  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.

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

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

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

[26]  Fan Yang,et al.  Keypoint-based copy-move detection scheme by adopting MSCRs and improved feature matching , 2017, Multimedia Tools and Applications.

[27]  Joachim Weickert,et al.  From Box Filtering to Fast Explicit Diffusion , 2010, DAGM-Symposium.

[28]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[29]  Z. Jane Wang,et al.  Median Filtering Forensics Based on Convolutional Neural Networks , 2015, IEEE Signal Processing Letters.

[30]  Zhihua Xia,et al.  A copy-move forgery detection method based on CMFD-SIFT , 2016, Multimedia Tools and Applications.

[31]  Zahid Mehmood,et al.  Copy-move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images. , 2017, Forensic science international.

[32]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[33]  Ling Liu,et al.  The algorithm of seamless image mosaic based on A‐KAZE features extraction and reducing the inclination of image , 2018 .

[34]  Hai Jin,et al.  SEPIM: Secure and Efficient Private Image Matching , 2016 .

[35]  Jiangqun Ni,et al.  A deep learning approach to detection of splicing and copy-move forgeries in images , 2016, 2016 IEEE International Workshop on Information Forensics and Security (WIFS).

[36]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[37]  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 .

[38]  Naif Alajlan,et al.  Deep Learning Approach for Car Detection in UAV Imagery , 2017, Remote. Sens..

[39]  Fan Yang,et al.  Copy-move forgery detection based on hybrid features , 2017, Eng. Appl. Artif. Intell..

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

[41]  Artur Janowski,et al.  Remote sensing and photogrammetry techniques in diagnostics of concrete structures , 2016 .

[42]  Wolfgang Förstner,et al.  A Trainable Markov Random Field for Low-Level Image Feature Matching with Spatial Relationships Ein trainierbares Markoff-Zufallsfeld für die Zuordnung lokaler Bildmerkmale unter Berücksichtigung ihrer räumlichen Beziehungen , 2013 .

[43]  Gul Muzaffer,et al.  A New Copy Move Forgery Detection Method Resistant to Object Removal with Uniform Background Forgery , 2016 .

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

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

[46]  Xiaoxia Wan,et al.  An improved method for SIFT-based copy-move forgery detection using non-maximum value suppression and optimized J-Linkage , 2017, Signal Process. Image Commun..

[47]  Xingming Sun,et al.  A security watermark scheme used for digital speech forensics , 2016, Multimedia Tools and Applications.