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Contrast Enhancement Estimation for Digital Image Forensics

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Published:22 May 2018Publication History
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Abstract

Inconsistency in contrast enhancement can be used to expose image forgeries. In this work, we describe a new method to estimate contrast enhancement operations from a single image. Our method takes advantage of the nature of contrast enhancement as a mapping between pixel values and the distinct characteristics it introduces to the image pixel histogram. Our method recovers the original pixel histogram and the contrast enhancement simultaneously from a single image with an iterative algorithm. Unlike previous works, our method is robust in the presence of additive noise perturbations that are used to hide the traces of contrast enhancement. Furthermore, we also develop an effective method to detect image regions undergone contrast enhancement transformations that are different from the rest of the image, and we use this method to detect composite images. We perform extensive experimental evaluations to demonstrate the efficacy and efficiency of our method.

References

  1. M. Barni, M. Fontani, and B. Tondi. 2012. A universal technique to hide traces of histogram-based image manipulations. In Proceedings of the ACM Workshop on Multimedia and Security. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Dimitri P. Bertsekas. 1996. Constrained Optimization and Lagrange Multiplier Methods,. Athena Scientific.Google ScholarGoogle Scholar
  3. S. Boyd and L. Vandenberghe. 2005. Convex Optimization. Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Yuri Boykov, Olga Veksler, and Ramin Zabih. 2001. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 11, 1222--1239. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Gang Cao, Yao Zhao, and Rongrong Ni. 2010. Forensic estimation of gamma correction in digital images. In Proceedings of the IEEE International Conference on Image Processing (ICIP’10).Google ScholarGoogle ScholarCross RefCross Ref
  6. Gang Cao, Yao Zhao, Rongrong Ni, and Xuelong Li. 2014. Contrast enhancement-based forensics in digital images. IEEE Transactions on Information Forensics and Security 9, 3, 515--525. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. G. Cao, Y. Zhao, R. Ni, and H. Tian. 2010. Anti-forensics of contrast enhancement in digital images. In Proceedings of the ACM Workshop on Multimedia and Security. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. CVX Research Inc. 2012. CVX: Matlab Software for Disciplined Convex Programming, Version 2.0 Beta. Available at from http://cvxr.com.Google ScholarGoogle Scholar
  9. Duc-Tien Dang-Nguyen, Cecilia Pasquini, Valentina Conotter, and Giulia Boato. 2015. RAISE—a raw images dataset for digital image forensics. In Proceedings of the ACM Multimedia Systems Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. H. Farid. 2001. Blind inverse gamma correction. IEEE Transactions on Image Processing 10, 10, 1102--1125. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Hany Farid (Ed.). 2016. Photo Forensics. MIT Press, Cambridge, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. P. Ferrara, T. Bianchiy, A. De Rosaz, and A. Piva. 2013. Reverse engineering of double compressed images in the presence of contrast enhancement. In Proceedings of the IEEE Workshop on Multimedia Signal Processing.Google ScholarGoogle Scholar
  13. M. Gonzalez and F. Woods. 2002. Digital Image Processing (2nd ed.). Prentice Hall. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Ralph P. Grimaldi. 1998. Discrete and Combinatorial Mathematics: An Applied Introduction (4th ed.). Addison-Wesley. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. E. Levina and P. Bickel. 2001. The earth mover’s distance is the Mallows distance: Some insights from statistics. In Proceedings of the IEEE International Conference on Computer Vision, Vol. 2. 251--256.Google ScholarGoogle Scholar
  16. Xufeng Lin, Xingjie Wei, and Chang-Tsun Liang. 2014. Two improved forensic methods of detecting contrast enhancement in digital images. In Proceedings of SPIE 9028: Media Watermarking, Security, and Forensics.Google ScholarGoogle Scholar
  17. Siwei Lyu, Xunyu Pan, and Xing Zhang. 2014. Exposing region splicing forgeries with blind local noise estimation. International Journal of Computer Vision 110, 2, 202--221. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jean Morales, Charles A. Micchelli, and Massimiliano Pontil. 2010. A family of penalty functions for structured sparsity. In Advances in Neural Information Processing Systems 23 (NIPS’10).Google ScholarGoogle Scholar
  19. M. C. Stamm and K. J. R. Liu. 2010. Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Transactions on Information Forensics and Security 5, 3, 492--506. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. C. Stamm and K. J. R. Liu. 2010. Forensic estimation and reconstruction of a contrast enhancement mapping. In Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP’10). 1698--1701.Google ScholarGoogle ScholarCross RefCross Ref
  21. C. Villani. 2008. Optimal Transport: Old and New. Springer, Berlin, Germany.Google ScholarGoogle Scholar
  22. Xing Zhang and Siwei Lyu. 2014. Blind estimation of pixel brightness transform. In Proceedings of the IEEE Conference on Image Processing (ICIP’14).Google ScholarGoogle ScholarCross RefCross Ref
  23. X. Zhang and S. Lyu. 2014. Estimating covariance matrix of correlated image noise. In Proceedings of the IEEE Conference on Image Processing (ICIP’14).Google ScholarGoogle Scholar
  24. Daniel Zoran and Yair Weiss. 2009. Scale invariance and noise in nature image. In Proceedings of the IEEE International Conference on Computer Vision.Google ScholarGoogle ScholarCross RefCross Ref

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          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 14, Issue 2
          May 2018
          208 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3210458
          Issue’s Table of Contents

          Copyright © 2018 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 22 May 2018
          • Revised: 1 January 2018
          • Accepted: 1 January 2018
          • Received: 1 July 2017
          Published in tomm Volume 14, Issue 2

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