Detecting Fake Image: A Review for Stopping Image Manipulation

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1192)


Data manipulation getting bigger threat day by day with the dynamic tech touch for the time being. Image is represented by underlying pixelated data consisting by its area elements. By the blessings and high availability of smart technology and device, images took important part of humans memorable life events. This is the evidence with most consideration for significance by human eye view. A true image can be a big game player both in social and practical situation. Moreover the technological manipulation of an image named fake image can make violation in major perspective consideration rather than any thinking flow or data to obtain the difference of right or wrong. Here the detailed information obtained from the conducted a literature review on the fake detection identification techniques is presented. The review paper contains information related to different fake image detection techniques instead of making detection true and false image. Several detection techniques had been studied like iris recognition, Support Vector Machine (SVM) and Purkinje image-based. Simultaneously we have considered biometric systems for security aspects as well as 2D to 3D image transformation problems. For web based applications demosaicing detection method and a colour image change splicing technology we have analyzed. Moreover we found underwater dam methods can be used for crack detection, where we focused on fake colorized image detection. Most importantly we have studied on fake smile identification to enrice image forgery technology stronger.


Fake image detection Fake smile GLCM 


  1. 1.
    Arulananth, T., Sujitha, M., Nalini, M., Srividya, B., Raviteja, K.: Fake shadow detection using local histogram of oriented gradients (HOG) features. In: 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA) (2017).
  2. 2.
    Bachtiar, M., Gusti, D., Wijaya, I., Hidajat, M.: Web-based application development for false images detection for multi images through demosaicing detection. In: 2018 International Conference on Information Management and Technology (ICIMTech) (2018).
  3. 3.
    Ballado, A., et al.: Philippine currency paper bill counterfeit detection through image processing using Canny Edge Technology. In: 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (2015).
  4. 4.
    Bhakt, N., Joshi, P., Dhyani, P.: A novel framework for real and fake smile detection from videos. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) (2018).
  5. 5.
    Bodade, R., Talbar, S., Batnagar, A.: Dynamic iris localisation: a novel approach suitable for fake iris detection. In: National Conference on Signal and Image Processing Applications (2009).
  6. 6.
    Bulla, A., Shreedarshan, K.: Fake shadow detection using local HOG features. In: 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (2016).
  7. 7.
    Chen, C., Wang, J., Zou, L., Fu, J., Ma, C.: A novel crack detection algorithm of underwater dam image. In: 2012 International Conference on Systems and Informatics (ICSAI2012) (2012).
  8. 8.
    Gadhiya, T., Roy, A., Mitra, S., Mall, V.: Use of discrete wavelet transform method for detection and localization of tampering in a digital medical image. In: 2017 IEEE Region 10 Symposium (TENSYMP) (2017).
  9. 9.
    Galbally, J., Marcel, S., Fierrez, J.: Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans. Image Process. 23(2), 710–724 (2014). Scholar
  10. 10.
    Gunadi, I., Harjoko, A., Wardoyo, R., Ramdhani, N.: Fake smile detection using linear support vector machine. In: 2015 International Conference on Data and Software Engineering (ICoDSE) (2015).
  11. 11.
    Guo, Y., Cao, X., Zhang, W., Wang, R.: Fake colorized image detection. IEEE Trans. Inf. Forensics Secur. 13(8), 1932–1944 (2018). Scholar
  12. 12.
    He, X., An, S., Shi, P.: Statistical texture analysis-based approach for fake iris detection using support vector machines. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 540–546. Springer, Heidelberg (2007). Scholar
  13. 13.
    Hou, B., Wei, Q., Zheng, Y., Wang, S.: Unsupervised change detection in SAR image based on gauss-log ratio image fusion and compressed projection. IEEE J. Sel. Top. Appl. Earth Obser. Remote Sens. 7(8), 3297–3317 (2014). Scholar
  14. 14.
    Lee, E.C., Park, K.R., Kim, J.: Fake iris detection by using purkinje image. In: Zhang, D., Jain, A.K. (eds.) ICB 2006. LNCS, vol. 3832, pp. 397–403. Springer, Heidelberg (2005). Scholar
  15. 15.
    Lv, S., Zhou, F., Wei, Z.: Train wheel tread defects detection based on image registration. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST) (2017).
  16. 16.
    Maatta, J., Hadid, A., Pietikainen, M.: Face spoofing detection from single images using micro-texture analysis. In: 2011 International Joint Conference on Biometrics (IJCB) (2011).
  17. 17.
    Malviya, A., Ladhake, S.: Region duplication detection using color histogram and moments in digital image. In: 2016 International Conference on Inventive Computation Technologies (ICICT) (2016).
  18. 18.
    Patil, R., Pete, D.: Image change detection using stereo imagery and digital surface mode. In: 2015 International Conference on Information Processing (ICIP) (2015).
  19. 19.
    Pravallika, P., Prasad, K.: SVM classification for fake biometric detection using image quality assessment: application to iris, face and palm print. In: 2016 International Conference on Inventive Computation Technologies (ICICT) (2016).
  20. 20.
    Pritam, D., Dewan, J.: Detection of fire using image processing techniques with LUV color space. In: 2017 2nd International Conference for Convergence in Technology (I2CT) (2017).
  21. 21.
    Rebhi, A., Abid, S., Fnaiech, F.: Texture defect detection method based on H-image and Hotteling model T\(^2\). In: 2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (2014).
  22. 22.
    Reno, A., David, D.: An application of image change detection-urbanization. In: 2015 International Conference on Circuits, Power and Computing Technologies, ICCPCT 2015 (2015).
  23. 23.
    Rosario-Torres, S., Velez-Reyes, M.: Speeding up the MATLAB\(^{\text{TM}}\) hyperspectral image analysis toolbox using GPUs and the jacket toolbox. In: 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (2009).
  24. 24.
    Sanchez, C., Niemeijer, M., Suttorp Schulten, M., Abramoff, M., van Ginneken, B.: Improving hard exudate detection in retinal images through a combination of local and contextual information. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2010).
  25. 25.
    Tan, C., Kumar, A.: Integrating ocular and iris descriptors for fake iris image detection. In: 2nd International Workshop on Biometrics and Forensics (2014).
  26. 26.
    Wang, W., Dong, J., Tan, T.: Effective image splicing detection based on image chroma. In: 2009 16th IEEE International Conference on Image Processing (ICIP) (2009).
  27. 27.
    Xinyu, T., Xuewu, Z., Xiaolong, X., Jinbao, S., Yan, X.: Methods for underwater sonar image processing in objection detection. In: 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC) (2017).
  28. 28.
    Yao, T., Dai, S., Wang, P., He, Y.: Image based obstacle detection for automatic train supervision. In: 2012 5th International Congress on Image and Signal Processing (2012).
  29. 29.
    Zhang, L., He, X.: Fake shadow detection based on SIFT features matching. In: 2010 WASE International Conference on Information Engineering (2010).
  30. 30.
    Jin-Yu, Z., Yan, C., Xian-Xiang, H.: Edge detection of images based on improved Sobel operator and genetic algorithms. In: 2009 International Conference on Image Analysis and Signal Processing (2009).

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.University Malaysia SarawakKuchingMalaysia

Personalised recommendations