Skip to main content

Advertisement

Springer Nature Link
Log in

Image splicing detection system using intensity-level multi-fractal dimension feature engineering and twin support vector machine based classifier

  • 1216: Intelligent and Sustainable Techniques for Multimedia Big Data Management for Smart Cities Services
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Electronic images have become an essential origin of information nowadays, the authenticity of images has become important. Several techniques used for forgery have come into existence like an intrusive method and non-intrusive method. Identification of image forgery is becoming more challenging day by day because of advancements in the processing of electronic images. Consequently, Image forensics is the core part of security applications designed to restore digital media loyalty and acceptance by revealing various methods of counterfeiting. The suggested work compares different feature extraction methods for forged images for the identification of spliced images. In classification techniques, a very difficult issue is to choose features to differentiate between classes. Various features are extracted from real and spliced images with the help of a method dependent on a spatial Gray level like Gray-Level Run Length Matrix etc. This paper describes the methodological considerations involved with the concept of multifractal analysis and with this its emphasis on the Differential Box-Counting method for fetching the Intensity-Level Multi-Fractal Dimension. Further, the research paper evaluates different state of the art in image splicing techniques, and it has been observed that Twin Support Vector Machine as classifier achieves significant efficiency with Intensity-Level Multi-Fractal Dimension as Feature extraction method as compared to other methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Abbreviations

ANN:

Artificial Neural Network

BP:

Back-Propagation

DBC:

Differential Box-Counting

DCM:

Difference Coefficient Matrices

DCT:

Discrete Cosine Transform

DWT:

Discrete Wavelet Transform

FD:

Fractal Dimension

GLCM:

Grey Level Co-occurrence Matrix

GLRLM:

Gray-Level Run Length Matrix

HOS:

high-order spectral

ILMFD:

Intensity-Level Multi-Fractal Dimension

IQMs:

Image Quality Metrics

JPEG:

Joint Photographic Expert Group

LBP:

Local Binary Pattern

LFD:

Local Fractal Dimension

MSB:

Most Significant Bit

MSLBP:

multi-scale Local Binary Patterns

QDCT:

Quaternion Discrete Cosine Transform

QPPs:

Quadratic Programming Problems

RBNN:

Radial-basis Neural network

RICLBP:

Rotation Invariant Co-occurrence along with LBPs

SAE:

stacked auto-encoder network

SIFT:

Scale Invariant Feature Transform

SPT:

Steerable Pyramid Transform

SVD:

Singular Value Decomposition

SVM:

Support Vector Machine

SVM-RFS:

Support Vector Machine Recursive Feature Selection

SVR:

support vector regression

SWT:

Stationary wavelet transforms

TPM:

Transition probability matrices

TSVM:

Twin Support Vector Machine

WLD:

weber local descriptor

References

  1. Agarwal S, Chand S (2018) Image forgery detection using co-occurrence-based texture operator in frequency domain. In: Progress in intelligent computing techniques: Theory, practice, and applications. Advances in intelligent systems and computing, vol 518. Springer, Singapore

    Google Scholar 

  2. Aizerman MA, Braverman EM, Rozoner LI (1964) Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control 25:821–837

    Google Scholar 

  3. Dong J, Wang W, Tan T (2013) CASIA Image Tampering Detection Evaluation Database. IEEE China Summit and International Conference on Signal and Information Processing, pp 422–426. Available: http://forensics.idealtest.org/

  4. Farid H (n.d.) Detecting digital forgeries using bispectral analysis. AI Lab, MIT Technical Report AIM-1999,1657

  5. Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graph Image Proc 4:172–179

    Article  Google Scholar 

  6. Han JG, Park TH, Moon YH, Eoma IK (2016) Efficient Markov feature extraction method for image splicing detection using maximization and threshold. Int J Electron Imaging 25(2):023031

    Article  Google Scholar 

  7. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3:610–621

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. He Z, Lu W, Sun W (2012) Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn 45(12):4292–4299

    Article  Google Scholar 

  10. Huang S, Cai N, Pacheco PP, Narandes S, Wang Y, Xu W (2018) Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics Proteomics 15:41–51. https://doi.org/10.21873/cgp.20063

    Article  Google Scholar 

  11. Jenadeleh M, Moghaddam ME (2016) Blind detection of region duplication forgery using fractal coding and feature matching. J Forensic Sci 61:623–636. https://doi.org/10.1111/1556-4029.13108

  12. Kamavisdar P, Saluja S (2013) A Survey on Image Classification Approaches and Techniques. Int J Adv Res Comput Commun Eng 2(1)

  13. Kanwal N, Girdhar A, Kaur, L., Bhullar JS (July 2019) Detection of digital image forgery using fast Fourier transform and local features. In: Proceeding of International Conference on Automation, Computational and Technology Management (ICACTM). IEEE, London. pp. 262–267

  14. Kaur M, Gupta S (Sept. 2016) A passive blind approach for image splicing detection based on DWT and LBP histograms. In: Proceedings of International Symposium on Security in Computing and Communication, Chandigarh. pp. 318–327

  15. Kumar U, Lahiri T (2013) Significant enhancement of object recognition efficiency using human cognition based decision clustering. Int J Comput Vis Image Proc 3(4):1–15

    Google Scholar 

  16. Lahiri T, Mishra H, Kumar U, Misra K (2009) Derivation of a protein-marker from heat-denatured protein-aggregate. Online J Bioinform 10(1):29–39

    Google Scholar 

  17. Latif EE, Taha A, Zayed H (2019) A passive Approach for detecting image splicing using deep Learning and haar wavelet transform. Int J Comput Netw Inf Secur:28–35

  18. Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224:59–67

    Article  Google Scholar 

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

  20. Li C, Ma Q, Xiao L, Li M, Zhang A (2017) Image splicing detection based on Markov features in QDCT domain. Neurocomputing 228:29–36

    Article  Google Scholar 

  21. Mandelbrot BB (1983) The fractal geometry of nature. W.H. Freeman, New York

    Book  Google Scholar 

  22. Muhammad G, Al-Hammadi MH, Hussain M, Bebis G (2014) Image forgery detection using steerable pyramid transform and local binary pattern. Mach Vis Appl 25(4):985–995

    Article  Google Scholar 

  23. Mushtaq S, Mir AH (2014) Novel method for image splicing detection. International Conference on Advances in Computing, Communications, and Informatics. https://doi.org/10.1109/ICACCI.2014.6968386

  24. Ng TT, Chang SF, Sun Q (2004) Blind detection of photomontage using higher order statistics. In: IEEE ISCAS. pp. 688–691

  25. Oommen RS, Jayamohan M, Sruthy S (2016) Using fractal dimension and singular values for ImageForgery detection and localization. Procedia Technol 24:1452–1459. https://doi.org/10.1016/j.protcy.2016.05.176

    Article  Google Scholar 

  26. Peng X (2010) TSVR: An efficient twin support vector machine for regression. Neural Networks 23(3):365–372. https://doi.org/10.1016/j.neunet.2009.07.002

  27. Pham NT, Lee J-W, Kwon G-R, Park C-S (2019) Hybrid Image-Retrieval Method for Image-Splicing Validation. Symmetry 11:83. https://doi.org/10.3390/sym11010083

    Article  Google Scholar 

  28. Pietronero L, Tosatti E (eds) (1986) Fractals in physics, Amsterdam

  29. Saleh SQ, Hussain M, Muhammad G, Bebis G (2015) Evaluation of image forgery detection using multi-scale weber local descriptors. J Int Symp Vis Comput 24(4):416–424

    Google Scholar 

  30. Schmidhube J (2015) Deep learning in neural networks: An overview. Elsevier 61:85–117

    Google Scholar 

  31. Shah A, El-Alfy E-SM (June 2018) Image splicing forgery detection using DCT coefficients with multi-scale LBP. In: Proceeding of International Conference on Computing Sciences and Engineering (ICCSE). IEEE, Kuwait City. pp. 1–6

  32. Sharma S, Ghanekar U (2018) A hybrid technique to discriminate Natural Images, Computer Generated Graphics Images, Spliced, Copy Move tampered images and Authentic images by using features and ELM classifier. Optik - Int J Light Electron Optics 172:470–483

    Article  Google Scholar 

  33. Takayasu H (1990) Fractals in physical sciences. Manchester University Press, Manchester

    MATH  Google Scholar 

  34. Ting Z, Rang-Ding W (2009) Copy-move forgery detection based on SVD in digital image. In: 2nd International Congress on Image and Signal Processing, CISP’09. pp. 0–4

  35. Tomer D, Agrawal S (2015) Twin support vector machine: a review from 2007 to 2014. Egypt Inform J 16:55–69

    Article  Google Scholar 

  36. Tripathi E, Kumar U, Tripathi SP, Yadav S (2019) Automated image splicing detection using texture based feature criterion and fuzzy support vector machine based classifier. International Conference on Cutting-edge Technologies in Engineering (ICon-CuTE), pp 81–86. https://doi.org/10.1109/ICon-CuTE47290.2019.8991470

  37. Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer, New York

    Book  MATH  Google Scholar 

  38. Vicsek T (1989) Fractal growth phenomena. World Scientific, Singapore

    Book  MATH  Google Scholar 

  39. Vidyadharan DS, Thampi SM (2017) Digital image forgery detection using compact multi-texture representation. J Intell Fuzzy Syst 32:3177–3188

    Article  Google Scholar 

  40. Wang W, Dong J, Tan T (2009) Effective image splicing detection based on image chroma. Image Processing. IEEE International Conference. pp. 1257–1260

  41. Yan J, Sun Y, Shanshan C, Hu X (2016) An improved box-counting method to estimate fractal dimension of images. J Appl Anal Comput 6(4):1114–1125

    MathSciNet  Google Scholar 

  42. Zhang Z, Wang G, Bian Y, Yu Z (2010) A novel model for splicing detection. In: IEEE 5th international conference on bio-inspired computing: Theories and applications pp. 962–965

Download references

Author information

Authors and Affiliations

  1. Institute of Engineering and Technology, Dr. A. P. J. Abdul Kalam Technical University, Uttar Pradesh, , Lucknow, India

    Esha Tripathi, Upendra Kumar & Surya Prakash Tripathi

Authors
  1. Esha Tripathi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Upendra Kumar
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Surya Prakash Tripathi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Upendra Kumar.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tripathi, E., Kumar, U. & Tripathi, S.P. Image splicing detection system using intensity-level multi-fractal dimension feature engineering and twin support vector machine based classifier. Multimed Tools Appl 82, 39745–39763 (2023). https://doi.org/10.1007/s11042-022-13519-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13519-2

Keywords