Multimedia Tools and Applications

, Volume 78, Issue 15, pp 21585–21611 | Cite as

New and efficient blind detection algorithm for digital image forgery using homomorphic image processing

  • Zeinab F. ElsharkawyEmail author
  • Safey A. S. Abdelwahab
  • Fathi E. Abd El-Samie
  • Moawad Dessouky
  • Sayed Elaraby


Digital image forgery detection is an important task in digital life as the image may be easily manipulated. This paper presents a novel blind tampering detection algorithm for images acquired from digital cameras and scanners. The algorithm is based on applying homomorphic image processing on each suspicious image to separate illumination from reflectance components. In natural images, it is known that the illumination component is approximately constant, while changes can be detected in tampered ones. Support Vector Machine (SVM) and Neural Network (NN) classifiers are used for classification of tampered images based on the illumination component, and their results are compared to obtain the best classifier performance. The Receiver Operating Characteristic (ROC) curve is used to depict the classifier performance. Three different color coordinate systems are tested with the proposed algorithm, and their results are compared to obtain the highest accuracy level. Joint Photographic Experts Group (JPEG) compressed images with different Quality Factors (QFs) are also tested with the proposed algorithm, and the performance of the proposed algorithm in the presence of noise is studied. The performance of the SVM classifier is better than that of the NN classifier as it is more accurate and faster. A 96.93% detection accuracy has been obtained regardless of the acquisition device.


Digital forensics Forgery detection Homomorphic image processing SVM and NN classifiers Color coordinate systems JPEG compression 



  1. 1.
    Alahmadi A, Hussain M, Aboalsamh H et al (2017) Passive detection of image forgery using DCT and local binary pattern. SIViP 11:81–88. CrossRefGoogle Scholar
  2. 2.
    Al-amri S, Kalyankar N, Khamitkar S (2010) A comparative study of removal noise from remote sensing image. IJCSI Int J Comput Sci 7:32–36Google Scholar
  3. 3.
    Alherbawi N, Shukur Z, Sulaiman R (2018) JPEG image classification in digital forensic via DCT coefficient analysis. Multimed Tools Appl 77:12805–12835CrossRefGoogle Scholar
  4. 4.
    Ashiba H, Awadallah K, El-Halfawy S, Abd El-Samie F (2008) Homomorphic enhancement of infrared images using the additive wavelet transform. Electromagnet Res C 1:123–130CrossRefGoogle Scholar
  5. 5.
    Bhartiya G, Jalal A (2017) Forgery detection using feature-clustering in recompressed JPEG images. Multimed Tools Appl 76:20799–20814CrossRefGoogle Scholar
  6. 6.
    Bianchini M, Scarselli F (2014) On the complexity of neural network classifiers: a comparison between shallow and deep architectures. IEEE Trans Neural Netw Learn Syst 25(8):1553–1565CrossRefGoogle Scholar
  7. 7.
    Birajdar G, Mankar V (2018) Blind image forensics using reciprocal singular value curve based local statistical features. Multimed Tools Appl 77:14153–14175CrossRefGoogle Scholar
  8. 8.
    Cheng Y, Xing X, Zhang H (2011) Blind detection of Eclosion forensics based on Curvelet image enhancement and edge detection. International Conference on Multimedia and Signal Processing 316–320Google Scholar
  9. 9.
    Chien C, Tesng D (2011) Color image enhancement with exact HSI color model. Int J Innov Comput Inform Contrl 7(12):6691–6710Google Scholar
  10. 10.
    Choi C, Lee M, Hyun D, Lee H (2012) Forged region detection for scanned images. Computer Science and Convergence. Lect Notes Electr Eng 114:687–694CrossRefGoogle Scholar
  11. 11.
    Chuang W, Swaminathan A, Wu M (2009) Tampering identification using empirical frequency response. IEEE International Conference on Acoustics, Speech and Signal Processing 1517–1520Google Scholar
  12. 12.
    Dixit P, Dixit M (2013) Study of JPEG image compression technique using discrete cosine transformation. Int J Interdisciplin Res Innov IJIRI 1(1):32–35Google Scholar
  13. 13.
    Elsharkawy Z, Abdelwahab S, Dessouky M, Elaraby S, Abd El-Samie F (2013) Accurate and robust identifying forged region method in scanned images. Int J Comput Appl 83:40–47Google Scholar
  14. 14.
    Elsharkawy Z, Abdelwahab S, Dessouky M, Elaraby S, Abd El-Samie F (2013) Identifying unique flatbed scanner characteristics for matching a scanned image to its source. CiiT Int J Digit Image Process 5:397–403Google Scholar
  15. 15.
    Fan D, Cheng M, Liu Y, Li T, Borji A (2017) Structure-measure: A New Way to Evaluate Foreground Maps. IEEE International conference on Computer Vision (ICCV) 4558–4567Google Scholar
  16. 16.
    Fan D, Gong C, Cao Y, Ren B, Cheng M & Borji A (2018) Enhanced-alignment measure for binary foreground map evaluation. International Joint Conference on Artificial Intelligence (IJCAI) 698–704Google Scholar
  17. 17.
    Fang Y, Dirik A, Sun X, Memon N (2009) Source class identification for DSLR and compact cameras. IEEE International Workshop on Multimedia Signal ProcessingGoogle Scholar
  18. 18.
    Farid H (2009) Image forgery detection. IEEE Signal Process Mag 26:16–25CrossRefGoogle Scholar
  19. 19.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett Elsevier Sci Inc 27:861–874CrossRefGoogle Scholar
  20. 20.
    Fridrich J (2009) Digital image forensics. IEEE Signal Process Mag 26(2):26–37CrossRefGoogle Scholar
  21. 21.
    Gupta B, Gupta M, Chadha B (2014) Image compression technique under JPEG by wavelets transformation. IJARCSSE 4:808–818Google Scholar
  22. 22.
    Hayat K, Qazi T (2017) Forgery detection in digital images via discrete wavelet and discrete cosine transforms. Comput Electr Eng 62:448–458CrossRefGoogle Scholar
  23. 23.
    Hilal A (2018) Image re-sampling detection through a novel interpolation kernel. Forensic Sci Int 287:25–35CrossRefGoogle Scholar
  24. 24.
    Huang F, Shi Y (2010) Detecting double JPEG compression with the same quantization matrix. IEEE Trans Inform Sec 5(4):848–856CrossRefGoogle Scholar
  25. 25.
    Khanna N, George T, Allebach J, Delp E (2008) Scanner identification with extension to forgery detection. SPIE Security, Forensics, Steganography, and Watermarking of Multimedia ContentsGoogle Scholar
  26. 26.
    Kumar R, Indrayan A (2011) Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr 48(4):277–287CrossRefGoogle Scholar
  27. 27.
    Lai Y, Huang T, Lin J, Lu H (2018) An improved block-based matching algorithm of copy-move forgery detection. Multimed Tools Appl 77:15093–15110CrossRefGoogle Scholar
  28. 28.
    Lin G, Chang M, Chen Y (2011) A passive-blind forgery detection scheme based on content-adaptive quantization table estimation. IEEE Trans Circ Syst Video Technol 21(4):421–434CrossRefGoogle Scholar
  29. 29.
    Liu W, He P, Li H, Yu H (2012) Improvement on the Algorithm of Homomorphic Filtering. International Conference on Electrical and Computer Engineering Advances in Biomedical Engineering 120–124Google Scholar
  30. 30.
    Liu A, Zhao Z, Zhang C, Su Y (2017) Median filtering forensics in digital images based on frequency-domain features. Multimed Tools Appl 76:22119–22132CrossRefGoogle Scholar
  31. 31.
    Nie F, Huang Y, Wang X, Huang H (2014) New primal SVM solver with linear computational cost for big data classifications. International Conference on Machine LearningGoogle Scholar
  32. 32.
    Pipariyal T, Agrawa S (2014) Statistical moments and fuzzy logic based classification of noise present in digital image. Int J Adv Res Comput Commun Eng 3(7):7453–7456Google Scholar
  33. 33.
    Poisel R, Tjoa S (2011) Forensics investigations of multimedia data: a review of the state-of-the-art. IEEE International Conference on IT Security Incident Management and IT Forensics 48- 61Google Scholar
  34. 34.
    Popescu A, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Technical report, TR2004–515. Dartmouth College, Computer ScienceGoogle Scholar
  35. 35.
    Popescu A, Farid H (2005) Exposing digital forgeries by detecting traces of re-sampling. IEEE Trans Signal Process 53(2):758–767CrossRefzbMATHGoogle Scholar
  36. 36.
    Pradeepthi T, Ramesh A (2011) Pipelined architecture of 2d-dct, quantization and zigzag process for JPEG image compression using VHDL. Int J VLSI Design Commun Syst 2(3):99–110CrossRefGoogle Scholar
  37. 37.
    Pradhan A (2012) Support vector machine-a survey. Int J Emerg Technol Adv Eng 2(8):82–85Google Scholar
  38. 38.
    Saleh S, Ibrahim H (2012) Mathematical equations for homomorphic filtering in frequency domain: a literature survey. International Conference on Information and Knowledge Management 74–77Google Scholar
  39. 39.
    Sharma B, Venugopalan K (2014) Comparison of neural network training functions for hematoma classification in brain CT images. IOSR-JCE 16(1):31–35CrossRefGoogle Scholar
  40. 40.
    Singh S, Chauhan D, Vatsa M, Singh R (2003) A robust skin color based face detection algorithm. Tamkang J Sci Eng 6(4):227–234Google Scholar
  41. 41.
    Sridhar S, Kumar P, Ramanaiah K (2014) Wavelet transform techniques for image compression – an evaluation. IJ Image Graph Sign Process 2:54–67Google Scholar
  42. 42.
    Srinivas V, Rani C, Madhu T (2014) Neural network based classification for speaker identification. Int J Signal Process Image Process Pattern Recogn 7:109–120Google Scholar
  43. 43.
    Sun P, Lang Y, Fan S et al (2018) Exposing splicing forgery based on color temperature estimation. Forensic Sci Int 289:1–11CrossRefGoogle Scholar
  44. 44.
    Sundermeyer M, Oparin I, Gauvain J et al. (2013) Comprasion of feedforward and recurrent neural network language models. IEEE international conference on Acoustics, Speech and Signal Processing 8430 – 8434Google Scholar
  45. 45.
    Yuan H (2011) Blind forensics of median filtering in digital images. IEEE Trans Inform Forensics Sec 6(4):1335–1345CrossRefGoogle Scholar
  46. 46.
    Zeng D, Liu K, Lai S, Zhou G, Zhao J (2014) Relation classification via convolutional deep neural network. International Conference on Computational Linguistics 2335–2344Google Scholar
  47. 47.
    Zheng J & Liu M (2009) A Digital Forgery Image Detection Algorithm Based on Wavelet Homomorphic Filtering. International Workshop on Digital Watermarking 152–160Google Scholar
  48. 48.
    Zhou L (2007) Study of digital forensics based on image content. Ph.D. Thesis, Beijing University of Posts and TelecommunicationsGoogle Scholar
  49. 49.
    Zhulong L, Xianghua L, Yuqian Z (2011) Passive Detection of Copy-paste Tampering for Digital Image Forensics. International Conference on Intelligent Computation Technology and Automation 663–65626Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Engineering Department, Nuclear Research CenterAtomic Energy AuthorityCairoEgypt
  2. 2.Department of Electronics and Electrical Communications, Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt

Personalised recommendations