article

Robust content authentication of gray and color images using lbp-dct markov-based features

Authors:

El-Sayed M. El-Alfy,

Muhammad A. QureshiAuthors Info & Claims

Multimedia Tools and Applications, Volume 76, Issue 12

Pages 14535 - 14556

https://doi.org/10.1007/s11042-016-3855-7

Published: 01 June 2017 Publication History

Abstract

This paper presents a robust method for passive content authentication of gray and color images. The idea is to capture local and global artifacts resulting from the image manipulation through combining intra-block Markov features in both LBP and DCT domains. An optimized support-vector machine with radial-basis kernel is then trained to classify images as being tampered or authentic. We intensively investigate the authentication capabilities of the proposed method for separate color channels and for various combinations of them. The proposed method, without and withfeature-level fusion, is evaluated on three benchmark datasets with a variety of forgery and post-processing operations. The results show that fusing Markov features from LBP and DCT modalities leads to consistent improvement in terms of detection accuracy as compared to the state-of-the-art passive methods. Furthermore, using information from all YCbCr channels help enhancing the detection rate to more than 99.7 % on CASIA TIDE v2.0 image collection.

References

[1]

Barni M, Costanzo A (2012) A fuzzy approach to deal with uncertainty in image forensics. Signal Process Image Commun 27(9):998---1010

Digital Library

[2]

Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10(3):226---245

Digital Library

[3]

Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121---167

Digital Library

[4]

Chamlawi R, Khan A, Usman I (2010) Authentication and recovery of images using multiple watermarks. Comput Electr Eng 36(3):578---584

Digital Library

[5]

Change CC, Lin CJ (2010) Libsvm a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm

Digital Library

[6]

Chen C, Shi YQ (2008) JPEG image steganalysis utilizing both intrablock and interblock correlations. In: IEEE International Symposium on Circuits and Systems (ISCAS), pp 3029---3032

[7]

Chen W, Shi YQ, Su W (2007) Image splicing detection using 2-d phase congruency and statistical moments of characteristic function. In: Proceeding of SPIE San Jose, CA, USA

[8]

Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge

Digital Library

[9]

Dong J, Wang W, Tan T, Shi YQ (2009). In: Kim H-J, Katzenbeisser S, Ho ATS (eds) Run-length and edge statistics based approach for image splicing detection. Digital Watermarking. Springer, Berlin, pp 76---87

Digital Library

[10]

Dong J, Wang W, Tan T (2013) CASIA image tampering detection evaluation database. In: IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), pp 422---426

[11]

El-Alfy ESM, Qureshi MA (2015) Combining spatial and dct based markov features for enhanced blind detection of image splicing. Pattern Anal Applic 18(3):713---723

Digital Library

[12]

Farid H (1999) Detecting digital forgeries using bispectral analysis. Technical report, Cambridge

Digital Library

[13]

Farid H, Lyu S (2003) Higher-order wavelet statistics and their application to digital forensics. In: Computer vision and pattern recognition workshop (CVPRW03), vol 8, pp 94---94

[14]

Fu D, Shi YQ, Su W (2006). In: Shi YQ, Jeon B (eds) Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition Digital Watermarking. Springer, Berlin, pp 177---187

Digital Library

[15]

Gonzalez RC, Woods RE (2008) Digital image processing, 3rd. Prentice-Hall, USA

Digital Library

[16]

Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1---3):389---422

Digital Library

[17]

Hall M, Witten I, Frank E (2011) Data mining: Practical machine learning tools and techniques. Kaufmann, Burlington

Digital Library

[18]

He Z, Sun W, Lu W, Lu H (2011) Digital image splicing detection based on approximate run length. Pattern Recogn Lett 32(12):1591---1597

Digital Library

[19]

He Z, Lu W, Sun W (2012a) Improved run length based detection of digital image splicing. Digital Forensics and Watermarking 349---360

Digital Library

[20]

He Z, Lu W, Sun W, Huang J (2012b) Digital image splicing detection based on markov features in DCT and DWT domain. Pattern Recogn 45(12):4292---4299

Digital Library

[21]

Hearst MA, Dumais S, Osman E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intelligent Syst Appl 13(4):18---28

Digital Library

[22]

Lee TY, Lin SD (2008) Dual watermark for image tamper detection and recovery. Pattern Recogn 41(11):3497---3506

Digital Library

[23]

Li L, Li S, Zhu H, Wu X (2014) Detecting copy-move forgery under affine transforms for image forensics. Comput Electr Eng 40(6):1951---1962

[24]

Mahalakshmi SD, Vijayalakshmi K, Priyadharsini S (2012) Digital image forgery detection and estimation by exploring basic image manipulations. Digit Investig 8(34):215---225

[25]

Mahdian B, Saic S (2010) A bibliography on blind methods for identifying image forgery. Signal Process Image Commun 25(6):389---399

Digital Library

[26]

Meeker M (2014) Internet trends. http://www.kpcb.com/internet-trends

[27]

Muhammad G, Al-Hammadi MH, Hussain M, Bebis G (2013) Image forgery detection using steerable pyramid transform and local binary pattern. Machine Vision and Applications 1---11

Digital Library

[28]

Ng TT, Chang SF (2004) A model for image splicing. In: Proceedings of IEEE international conference on image processing, vol 2, pp 1169---1172

[29]

Ng TT, Chang SF, Sun Q (2004a) Blind detection of photomontage using higher order statistics. In: Proceedings of International Symposium on Circuits and Systems (ISCAS), vol 5, pp 688---691

[30]

Ng TT, Chang SF, Sun Q (2004b) A data set of authentic and spliced image blocks. Columbia University, ADVENT Technical Report 203---204

[31]

Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971---987

Digital Library

[32]

Poynton CA (1996) A technical introduction to digital video. John Wiley & Sons, New Jersey

Digital Library

[33]

Prathap I, Natarajan V, Anitha R (2014) Hybrid robust watermarking for color images. Comput Electr Eng 40(3):920---930

[34]

Qazi T, Hayat K, Khan S, Madani S, Khan I, Kolodziej J, Li H, Lin W, Yow KC, Xu CZ (2013) Survey on blind image forgery detection. IET Image Process 7(7):660---670

[35]

Redi JA, Taktak W, Dugelay JL (2011) Digital image forensics: a booklet for beginners. Multimedia Tools Appl 51(1):133---162

Digital Library

[36]

Shi YQ, Xuan G, Zou D, Gao J, Yang C, Zhang Z, Chai P, Chen W, Chen C (2005) Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image and neural network. In: IEEE International Conference on Multimedia and Expo ICME

[37]

Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: Proceedings of the 9th Workshop on Multimedia and Security, pp 51---62

Digital Library

[38]

Wang W, Dong J, Tan T (2009) Effective image splicing detection based on image chroma. In: 16th IEEE International Conference on Image Processing (ICIP), pp 1257---1260

Digital Library

[39]

Wang W, Dong J, Tan T (2010) Image tampering detection based on stationary distribution of Markov chain. In: 17th IEEE International Conference on Image Processing (ICIP), pp 2101---2104

[40]

Zhang Y, Zhao C, Pi Y, Li S (2012) Revealing image splicing forgery using local binary patterns of DCT coefficients. In: Communications, Signal Processing, and Systems. Springer, pp 181---189

[41]

Zhao X, Li J, Li S, Wang S (2011) Detecting digital image splicing in chroma spaces. In: Digital Watermarking. Springer, pp 12---22

[42]

Zhao X, Wang S, Li S, Li J (2012) A comprehensive study on third order statistical features for image splicing detection. Digital Forensics and Watermarking 243---256

Digital Library

[43]

Zhu Y, Shen X, Chen H (2015) Copy-move forgery detection based on scaled orb. Multimedia Tools and Applications 1---13

Digital Library

Cited By

Kaur NJindal NSingh K(2022)An improved approach for single and multiple copy-move forgery detection and localization in digital imagesMultimedia Tools and Applications10.1007/s11042-022-13105-681:27(38817-38847)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-13105-6
Kaur NJindal NSingh K(2020)A passive approach for the detection of splicing forgery in digital imagesMultimedia Tools and Applications10.1007/s11042-020-09275-w79:43-44(32037-32063)Online publication date: 25-Aug-2020
https://dl.acm.org/doi/10.1007/s11042-020-09275-w
Yang YKong XFeng C(2018)Double-compressed JPEG images steganalysis with transferring featureMultimedia Tools and Applications10.1007/s11042-018-5734-x77:14(17993-18005)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s11042-018-5734-x

Robust content authentication of gray and color images using lbp-dct markov-based features
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 76, Issue 12

June 2017

833 pages

ISSN:1380-7501

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Copyright © Copyright © 2017 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2017

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Cited By

Kaur NJindal NSingh K(2022)An improved approach for single and multiple copy-move forgery detection and localization in digital imagesMultimedia Tools and Applications10.1007/s11042-022-13105-681:27(38817-38847)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-13105-6
Kaur NJindal NSingh K(2020)A passive approach for the detection of splicing forgery in digital imagesMultimedia Tools and Applications10.1007/s11042-020-09275-w79:43-44(32037-32063)Online publication date: 25-Aug-2020
https://dl.acm.org/doi/10.1007/s11042-020-09275-w
Yang YKong XFeng C(2018)Double-compressed JPEG images steganalysis with transferring featureMultimedia Tools and Applications10.1007/s11042-018-5734-x77:14(17993-18005)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s11042-018-5734-x

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