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Forensic Analysis of Linear and Nonlinear Image Filtering Using Quantization Noise

Published:08 March 2016Publication History
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Abstract

The availability of intelligent image editing techniques and antiforensic algorithms, make it convenient to manipulate an image and to hide the artifacts that it might have produced in the process. Real world forgeries are generally followed by the application of enhancement techniques such as filtering and/or conversion of the image format to suppress the forgery artifacts. Though several techniques evolved in the direction of detecting some of these manipulations, additional operations like recompression, nonlinear filtering, and other antiforensic methods during forgery are not deeply investigated. Toward this, we propose a robust method to detect whether a given image has undergone filtering (linear or nonlinear) based enhancement, possibly followed by format conversion after forgery. In the proposed method, JPEG quantization noise is obtained using natural image prior and quantization noise models. Transition probability features extracted from the quantization noise are used for machine learning based detection and classification. We test the effectiveness of the algorithm in classifying the class of the filter applied and the efficacy in detecting filtering in low resolution images. Experiments are performed to compare the performance of the proposed technique with state-of-the-art forensic filtering detection algorithms. It is found that the proposed technique is superior in most of the cases. Also, experiments against popular antiforensic algorithms show the counter antiforensic robustness of the proposed technique.

References

  1. P. Bas, T. Filler, and T. Pevny. 2011. Break our steganographic system: The ins and outs of organizing BOSS. In Proceedings of the 13th International Conference on Information Hiding, Lecture Notes in Computer Science, Vol. 6958. 59--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. G. Bhatnagar, Q. M. J.n Wu, and P. K. Atrey. 2013. Secure randomized image watermarking based on singular value decomposition. ACM Trans. Multimedia Comput. Commun. Appl. 10, 1 (Dec. 2013), 4:1--4:21. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. H. Cao and A. C. Kot. 2012. Manipulation detection on image patches using fusionboost. IEEE Trans. Inf. Forensic Security 7, 3 (June 2012), 992--1002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. C.-C. Chang and C.-J. Lin. 2011. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 3 (2011), 27:1--27:27. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. C. Chen, J. Ni, and J. Huang. 2013. Blind detection of median filtering in digital images: A difference domain based approach. IEEE Trans. Inf. Forensic Security 22, 12 (December 2013), 4699--4710. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. C. Chen and Y. Q. Shi. 2008. JPEG image steganalysis utilizing both intrablock and interblock correlations. In Proceedings of the IEEE Interntional Symposium on Circuits and Systems. 3029--3032.Google ScholarGoogle Scholar
  7. C. Chen, Y. Q. Shi, and W. Su. 2008. A machine learning based scheme for double JPEG compression detection. In Proceedings of the IEEE International Conference on Pattern Recognition. 1--4.Google ScholarGoogle Scholar
  8. V. Conotter, P. Comesana, and F. Perez-Gonzalez. 2013a. Forensic analysis of full-frame linearly filtered JPEG images. In Proceedings of the IEEE International Conference on Image Processing. 4517--4521.Google ScholarGoogle Scholar
  9. V. Conotter, P. Comesana, and F. Perez-Gonzalez. 2013b. Joint detection of full-frame linear filtering and JPEG compression in digital images. In Proceedings of the IEEE International Workshop on Information Forensics and Security. 156--161.Google ScholarGoogle Scholar
  10. D. Cozzolino, D. Gragnaniello, and L. Verdoliva. 2014. Image forgery detection through residual-based local descriptors and block-matching. In Proceedings of the IEEE International Conference on Image Processing. 5297--5301.Google ScholarGoogle Scholar
  11. W. Fan, K. Wang, F. Cayre, and Z. Xiong. 2013. JPEG anti-forensics using non-parametric DCT quantization noise estimation and natural image statistics. In Proceedings of the 1st ACM Workshop on Information Hiding and Multimedia Security. ACM, 117--122. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. W. Fan, K. Wang, F. Cayre, and Z. Xiong. 2015. Median filtered image quality enhancement and anti-forensics via variational deconvolution. IEEE Trans. Inf. Forensic Security 10, 5 (April 2015), 1076--1091.Google ScholarGoogle Scholar
  13. H. Farid. 2009. Exposing digital forgeries from JPEG ghosts. IEEE Trans. Inf. Forensics Security 4, 1 (March 2009), 154--160. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. J. Fridrich and J. Kodovsky. 2012. Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensic Security 7, 3 (2012), 868--882. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. T. Gloe and R. Bohme. 2010. The Dresden image database for benchmarking digital image forensics. J. Digital Forensic Pract. 3, 2--4 (2010), 150--159. Available at http://forensics.inf.tu-dresden.de/ddimgdb/.Google ScholarGoogle ScholarCross RefCross Ref
  16. K. He, J. Sun, and X. Tang. 2013. Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35, 6 (June 2013), 167--200. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. F. Huang, J. Huang, and Y. Q. Shi. 2010. Detecting double JPEG compression with the same quantization matrix. IEEE Trans. Inf. Forensics Security 5, 4 (2010), 848--856. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. X. Kang, M. C. Stamm, A. Peng, and K. J. R. Liu. 2013. Robust median filtering forensics using an autoregressive model. IEEE Trans. Inf. Forensic Security 8, 9 (September 2013), 1456--1468. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. M. Kirchner and R. Bohme. 2008. Hiding traces of resampling in digital images. IEEE Trans. Inf. Forensic Security 3, 4 (November 2008), 582--592. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. Kirchner and J. Fridrich. 2010. On detection of median ltering in digital images. In Proceedings of the SPIE, Electron. Imaging, Media Forensics and Security II, Vol. 7541. 112.Google ScholarGoogle Scholar
  21. J. Kodovsky, J. Fridrich, and V. Holub. 2012. Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensic Security 7, 2 (April 2012), 432--444. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. Lai and R. Böhme. 2011. Countering counter forensics: The case of JPEG compression. In Proceedings of the International Conference on Information Hiding. 285--298. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. A. Lathey and P. K. Atrey. 2015. Image enhancement in encrypted domain over cloud. ACM Trans. Multimedia Comput. Commun. Appl. 11, 3 (Feb. 2015), 38:1--38:24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. S. Z. Li and S. Singh. 2009. Markov Random Field Modeling in Image Analysis. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. S. Z. Li. 1995. Markov Random Field Modeling in Computer Vision. Springer-Verlag, London, UK. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Q. Liu, A. H. Sung, and M. Qiao. 2011. A method to detect JPEG-based double compression. Advances in Neural Networks, Lecture Notes in Computer Science, Vol. 6676 Springer, 466--476. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Q. Liu, A. H. Sung, M. Qiao, Z. Chen, and B. Ribeiro. 2010. An improved approach to steganalysis of JPEG images. Inf. Sci. 180, 9 (2010), 1643--1655. Software available at http://www.shsu.edu/∼qxl005/New/Downloads. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. T. Pevny, P. Bas, and J. Fridrich. 2010. Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensic Security 5, 2 (June 2010), 215--224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. T. Pevny and J. Fridrich. 2007. Merging Markov and DT features for multiclass JPEG steganalysis. In Proceedings of SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents. 3:1--3:14.Google ScholarGoogle Scholar
  30. G. Puglisi, A. R. Bruna, F. Galvan, and S. Battiato. 2013. First JPEG quantization matrix estimation based on histogram analysis. In Proceedings of the IEEE International Conference on Image Processing. 4502--4506.Google ScholarGoogle Scholar
  31. X. Qiu, H. Li, W. Luo, and J. Huang. 2014. A universal image forensic strategy based on steganalytic model. In Proceedings of the 2nd ACM Workshop on Information Hiding and Multimedia Security (IHMMSec’14). ACM, 165--170. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. H. Ravi, A. V. Subramanyam, B. Avinash Kumar, and G. Gupta. 2014. Compression noise based video forgery detection. In Proceedings of the IEEE International Conference on Image Processing. 5352--5356.Google ScholarGoogle Scholar
  33. H. Ravi, A. V. Subramanyam, and S. Emmanuel. 2015. Spatial domain quantization noise based image filtering detection. In Proceedings of the IEEE International Conference on Image Processing.Google ScholarGoogle Scholar
  34. M. A. Robertson and R. L. Stevenson. 2005. DCT quantization in compressed images. IEEE Trans. Circ. Syst. Video Technol. 13 (Jan. 2005), 27--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. G. Schaefer and M. Stich. 2004. UCID—An uncompressed colour image database. In Proceedings of SPIE Storage and Retrieval Methods and Applications for Multimedia. 472--480.Google ScholarGoogle Scholar
  36. M. C. Stamm, M. Wu, and K. J . R. Liu. 2013. Information forensics: An overview of the first decade. IEEE Access 1 (May 2013), 167--200.Google ScholarGoogle Scholar
  37. G. Valenzise, V. Nobile, M. Tagliasacchi, and S. Tubaro. 2011. Countering JPEG anti-forensics. In Proceedings of the IEEE International Conference on Image Processing. 1949--1952.Google ScholarGoogle Scholar
  38. L. Verdoliva, D. Cozzolino, and G. Poggi. 2014. A feature-based approach for image tampering detection and localization. In Proceedings of the IEEE International Workshop on Information Forensics and Security. 149--154.Google ScholarGoogle Scholar
  39. R. Ware and F. Lad. 2003. Approximating the Distribution for Sums of Products of Normal Variables. Technical Report UCDMS. University of Canterbury, England.Google ScholarGoogle Scholar
  40. Z.-H. Wu, M. C. Stamm, and K. J. R. Liu. 2013. Anti-forensics of median filtering. In Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing. 3043--3047.Google ScholarGoogle ScholarCross RefCross Ref
  41. H. Zeng, T. Qin, X. Kang, and L. Liu. 2014. Countering anti-forensics of median filtering. In Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing. 2704--2708.Google ScholarGoogle Scholar
  42. Y. Zhang, S. Li, S. Wang, and Y. Q. Shi. 2014. Revealing the traces of median filtering using high-order local ternary patternsl. IEEE Signal Process. Lett. 21, 3 (March 2014), 275--280.Google ScholarGoogle ScholarCross RefCross Ref
  43. D. Zoran and Y. Weiss. 2011. From learning models of natural image patches to whole image restoration. In Proceedings of the IEEE International Conference on Computer Vision. 479--486. Google ScholarGoogle ScholarDigital LibraryDigital Library

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