Anti-Forensics of JPEG Images using Interpolation

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Author(s)

Saurabh Agarwal 1,* Satish Chand 1

1. Dept. of Computer Engineering, Netaji Subhash Institute of Technology, Sector-3, Dwarka, New Delhi, 110078, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2015.12.02

Received: 1 Jul. 2015 / Revised: 14 Aug. 2015 / Accepted: 7 Oct. 2015 / Published: 8 Nov. 2015

Index Terms

Anti-forensics, JPEG compression, Interpolation, compression artifacts, Image quality

Abstract

The quantization artifacts and blocking artifacts are the two significant properties for identifying the forgery in a JPEG compressed image. There are some techniques for JPEG compressed images that can remove these artifacts resulting no traces for forgery. These methods are referred as anti-forensic methods. A forger may perform some post-operations to disturb the underlying statistics of JPEG images to fool current forensic techniques. These methods create noise and reduce the image quality. In this paper we apply three different interpolation techniques namely nearest neighbor, bilinear and bicubic techniques to remove JPEG artifacts. The experimental results show that the bicubic interpolated images are found to be of better quality as compare to the nearest neighbor and bilinear interpolated images with no JPEG artifacts. For quality analysis of these interpolation methods on the images three popular quality metric are used. The proposed method is very simple to perform. This interpolation based method is applicable to both single and double JPEG compression.

Cite This Paper

Saurabh Agarwal, Satish Chand,"Anti-Forensics of JPEG Images using Interpolation", IJIGSP, vol.7, no.12, pp.10-17, 2015. DOI: 10.5815/ijigsp.2015.12.02

Reference

[1]Z. Fan, R. L. de Queiroz, Identification of bitmap compression history: JPEG detection and quantizer estimation, IEEE Transaction on Image Processing, vol. 12, no. 2, pp. 230–235, Feb. 2003.

[2]H. Farid, Exposing digital forgeries from JPEG ghosts, IEEE Transaction on Information Forensics and Security, vol. 4, no. 1, pp. 154–160, Mar. 2009.

[3]X. Feng, G. Doërr, JPEG recompression detection, Proceedings of the SPIE-Media Forensics and Security II, vol. 7541 of , 75410J, Jan. 2010.

[4]F. Huang, J. Huang, Y. Q. Shi, Detecting double JPEG compression with the same quantization matrix, IEEE Transaction on Information Forensics and Security, vol. 5, no. 4, pp. 848–856, Dec. 2010.

[5]A. Bianchi, T. De Rosa, A. Piva, Improved DCT coefficient analysis for forgery localization in JPEG images, in proceeding of the international conference on Acoustics, Speech, and Signal Processing, Prague, Czech Republic, May 2011.

[6]M. Stamm, S. Tjoa, W. S. Lin, K. J. Ray Liu, Antiforensics of JPEG compression, in proceeding of the international conference on Acoustics, Speech, and Signal Processing, pp. 1694–1697, 2010.

[7]H. Li, W. Luo, J. Huang, Countering anti-JPEG compression forensics, in Proceeding of the 19th. IEEE International Conference on Image Processing, pp. 241-244, IEEE, 2012.

[8]Milani S, Tagliasacchi M, Tubaro S, Antiforensic attacks to Benford's law for detection of double compressed images, in proceeding of the international conference on Acoustics, Speech, and Signal Processing, pp 500-505, 2013.

[9]C. Pasquini and G. Boato, JPEG compression anti-forensics based on first significant digit distribution, Proceeding of the IEEE international workshop on multimedia signal processing, pp 500-505, 2013.

[10]W. Fan, K. Wang, F. Cayre, Z. Xiong, JPEG anti-forensics using non-parametric DCT quantization noise estimation and natural image statistics, in Proceeding of the ACM international Workshop Information Hiding and Multimedia Security, pp. 117–122, 2013.

[11]J. He, Z. Lin, L. Wang, X. Tang, Detecting doctored JPEG images via DCT coefficient analysis, in Proceeding of the European Conference on Computer Vision (ECCV), vol. 3953, pp. 423-435, Mar. 2006.

[12]Z. Lin, J. He, X. Tang, C.K. Tang, Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis, Pattern Recognition, vol. 42, no.11, pp. 2492-2501, 2009.

[13]Y.-L. Chen, C.-T. Hsu, Detecting recompression of JPEG images via periodicity analysis of compression artifacts for tampering detection, IEEE Transaction on Information Forensics and Security, vol. 6, no. 2, pp. 396-406, Jun. 2011.

[14]S. Ye, Q. Sun, E.-C. Chang, Detecting digital image forgeries by measuring inconsistencies of blocking artifact, in Proceeding of the IEEE International Conference of Multimedia Expo, pp. 12-15, Jul. 2007.

[15]Zheng Er-gong, Ping Xi-jian, Passive-blind forensics for a class of JPEG image forgery, Journal of Electronics information technology, vol.32, no.2, p.394, 2010.

[16]W. Li, Y. Yuan, N. Yu, Passive detection of doctored JPEG image via block artifact grid extraction, Signal Processing, vol. 89, no. 9, pp. 1821-1829, 2009.

[17]M. Barni, A. Costanzo, L. Sabatini, Identification of cut & paste tampering by means of double-JPEG detection and image segmentation, Proceeding of the IEEE International Symposium of Circuits System, pp. 1687-1690, Jun. 2010.

[18]Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.

[19]A. Mittal, A. K. Moorthy, A. C. Bovik, No-Reference Image Quality Assessment in the Spatial Domain, IEEE Transactions on Image Processing, 2012.

[20]S.Kumar, J. V. Desai, and S.D. Mukherjee. "Copy Move Forgery Detection in Contrast Variant Environment using Binary DCT Vectors.", IJIGSP Vol.7, No.6, May 2015.

[21]M. K. Johnson, H. Farid, Exposing digital forgeries in complex lighting environments, IEEE Transaction on Information Forensics and Security, vol. 2, no. 3, pp. 450-461, Sep. 2007.

[22]J. Lukas, J. Fridrich, M. Goljan, Detecting digital image forgeries using sensor pattern noise, Proceeding of the SPIE Electronic imaging, security, stegnography, and watermarking of multimedia contents VIII, vol. 6072. 2006.

[23]P. Ferrara, T. Bianchi, A.D. Rosa, A. Piva, Image forgery localization via fine-grained analysis of CFA artifacts, IEEE Transaction on Information Forensics and Security, vol 7, no 5, pp. 1566-1577, 2012.

[24]D. Fu, Y. Q. Shi, W. Su, A generalized Benford's law for JPEG coefficients and its applications in image forensics, Proceeding of the SPIE, vol. 6505, pp. 39–48, Jan. 28 – Feb. 1, 2009.

[25]W. Fan, K. Wang, F. Cayre, Z. Xiong, JPEG AntiForensics with improved tradeoff between forensic undetectability and image quality, IEEE Transaction on Information Forensics and Security, vol.9, no.8, August 2014.

[26]Kaimal, A.B., Anitha, J., Manimurgan, S., A modified anti-forensics technique for removing detectable traces from digital images, proceeding of the international Conference on computer, communication and informatics, pp.1-4, 2013.

[27]J.A. Parker, R.V. Kenyon, D.E. Troxel, Comparison of interpolating methods for image re-sampling, IEEE Transactions on Medical Imaging, pp. 31–39, 1983.

[28]R. Keys, Cubic Convolution Interpolation for DigitalImage Processing, IEEE Transaction on Acoustics, speech and signal processing, vol ASSP-29, No. 6, Dec 1981. 

[29]A G. Weber, "The USC-SIPI Image Database: USC SIPI Report 315", California, October 1997. 

[30]P. Sutthiwan, Y.Q. Shi, "Anti-forensics of double JPEG compression detection," in Proceedings International Workshop of Digital Forensics Watermarking, 2011, pp. 411-424.