Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features

This article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network CNN-based method to accurately localize the tampered regions, which combines color filter array CFA features. The CFA interpolation algorithm introduces the correlation and consistency among the pixels, which can be easily destroyed by most image processing operations. The proposed CNN method can effectively distinguish the traces caused by copy-move forgeries and some post-processing operations. Additionally, it can utilize the classification result to guide the feature extraction, which can enhance the robustness of the learned features. This article, per the authors, tests the proposed method in several experiments. The results demonstrate the efficiency of the method on different forgeries and quantifies its robustness and sensitivity.

[1]  Chun-Shien Lu,et al.  Structural digital signature for image authentication: an incidental distortion resistant scheme , 2003, IEEE Trans. Multim..

[2]  Deepa Kundur,et al.  Digital watermarking for telltale tamper proofing and authentication , 1999, Proc. IEEE.

[3]  Pravin Kakar,et al.  Exposing Postprocessed Copy–Paste Forgeries Through Transform-Invariant Features , 2012, IEEE Transactions on Information Forensics and Security.

[4]  Peter B. Delahunt,et al.  Perceptual assessment of demosaicing algorithm performance , 2002, Proc. IEEE.

[5]  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 .

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

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

[8]  R.W. Schafer,et al.  Demosaicking: color filter array interpolation , 2005, IEEE Signal Processing Magazine.

[9]  Jing Dong,et al.  Deep learning for steganalysis via convolutional neural networks , 2015, Electronic Imaging.

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

[11]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[12]  Xiaochun Cao,et al.  MIFT: A Mirror Reflection Invariant Feature Descriptor , 2009, ACCV.

[13]  Yi Li,et al.  Convolutional Neural Networks for No-Reference Image Quality Assessment , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Jie Hu,et al.  An Improved Lexicographical Sort Algorithm of Copy-move Forgery Detection , 2011, 2011 Second International Conference on Networking and Distributed Computing.

[15]  Alessandro Piva,et al.  Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts , 2012, IEEE Transactions on Information Forensics and Security.

[16]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

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

[18]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[19]  Yao Zhao,et al.  Recapture Image Forensics Based on Laplacian Convolutional Neural Networks , 2016, IWDW.

[20]  A. Piva.  An Overview on Image Forensics , 2013 .

[21]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

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

[23]  Belhassen Bayar,et al.  A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer , 2016, IH&MMSec.

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

[25]  Yanjun Cao,et al.  A Robust Detection Algorithm for Region Duplication in Digital Images , 2011 .

[26]  Christian Riess,et al.  On rotation invariance in copy-move forgery detection , 2010, 2010 IEEE International Workshop on Information Forensics and Security.

[27]  Shiguo Lian,et al.  A passive image authentication scheme for detecting region-duplication forgery with rotation , 2011, J. Netw. Comput. Appl..

[28]  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).

[29]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[30]  Paolo Bestagini,et al.  Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks , 2017, J. Vis. Commun. Image Represent..

[31]  Saeed Shiry Ghidary,et al.  Convolutional Neural Networks for Image Processing: An Application in Robot Vision , 2003, Australian Conference on Artificial Intelligence.

[32]  Heung-Kyu Lee,et al.  Detection of Copy-Rotate-Move Forgery Using Zernike Moments , 2010, Information Hiding.

[33]  Rainer Böhme,et al.  The Dresden Image Database for Benchmarking Digital Image Forensics , 2010, J. Digit. Forensic Pract..