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.
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
- H. Farid. 2009. Exposing digital forgeries from JPEG ghosts. IEEE Trans. Inf. Forensics Security 4, 1 (March 2009), 154--160. Google Scholar
Digital Library
- J. Fridrich and J. Kodovsky. 2012. Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensic Security 7, 3 (2012), 868--882. Google Scholar
Digital Library
- 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 Scholar
Cross Ref
- K. He, J. Sun, and X. Tang. 2013. Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35, 6 (June 2013), 167--200. Google Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- S. Z. Li and S. Singh. 2009. Markov Random Field Modeling in Image Analysis. Springer. Google Scholar
Digital Library
- S. Z. Li. 1995. Markov Random Field Modeling in Computer Vision. Springer-Verlag, London, UK. Google Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
- 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 Scholar
Digital Library
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- R. Ware and F. Lad. 2003. Approximating the Distribution for Sums of Products of Normal Variables. Technical Report UCDMS. University of Canterbury, England.Google Scholar
- 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 Scholar
Cross Ref
- 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 Scholar
- 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 Scholar
Cross Ref
- 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 Scholar
Digital Library
Index Terms
Forensic Analysis of Linear and Nonlinear Image Filtering Using Quantization Noise
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