Forged Image Detection by Analyzing Edge, Visual Saliency and Textural Features using SVM Classifier

Authors(1) :-R Gomathi

Nowadays, the use of Digital images is everywhere, in the magazines, in newspapers, in hospitals, in shopping malls and all over the Internet. As the development in technology is increasing day by day, at the same time the trust in images is decreasing day by day. Most common type of Image forgery is Image composition, which is also termed by the name Image Splicing. Combination of two or more images to generate a fake image is known as Image composition. It becomes very hard to differentiate between real image and fake images because of the presence of various powerful editing software. As a result, in most of the cases, there is a need to prove whether the images are real or not. This paper describes a technique for detecting forgery of composite images using Support Vector Machine (SVM) classifier. In the state of art of work, the forged image is detected by extracting Edge and Visual Saliency features. The proposed work detects the forged image by extracting Textural features in addition with Edge and Visual Saliency features. By using True Negative (TN) rate, True Positive (TP) rate and Accuracy parameters, it is found that the proposed method gives improved efficiency when compared with the existing methods.

Authors and Affiliations

R Gomathi
Department of ECE, University College of Engineering Dindigul, Dindigul, Tamilnadu, India

Digital Image, Support Vector Machine, Textural Features

  1. Ajaz Hussain Mir and Saba Mushtaq, (2014), ‘Digital Image Forgeries and Passive Image Authentication Techniques’, Proc. International Journal of Advanced Science and Technology.
  2. Anita  Sahani,  Srilatha,  K.(2014),  ‘Image  Forgery  Detection  using  SVM Classifier’,  Proc.  International Journal of  Advanced  Research  in  Electrical Electronics and Instrumentation Engineering.
  3. Cao, X. Hou,Y.Qu, Y. Zhang, W. Zhao, H. and Zhang,C. (2010), ‘Detecting and extracting the photo composites using planar homography and graph cut’, Proc. IEEE Trans Inf. Forensics Security.
  4. Chen, W. Shi, Y. and Su, W. (2007), ‘Image splicing detection using 2-d phase congruency and statistical moments of characteristic function’, Proc. Of SPIE electronic imaging: security, stenography, and watermarking of multimedia contents.
  5. Fang,Z. Wang, S. and Zhang,X. (2010), ‘Image splicing detection using color edge inconsistency’, Int. Conf. on Multimedia Information Networking and Security (MINES).
  6. Fu, D. Shi, Y. (2006), ‘Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition’, Proc. of International workshop on digital watermarking.
  7. Ng, T. Chang, S. and Sun,Q. (2004), ‘Blind detection of photomontage using higher order statistics’, Proc. IEEE International symposium on circuits and systems (ISCAS).
  8. Mandeep Kaur, ER. Tamana Sharma (2016), ‘Forgery detection of spliced images using machine learning classifiers and color illumination’, Proc. International Journal of Innovative Research in Science, Engineering and Technology.
  9. Pradyumna Deshpande, Prashasti Kanikar, (2012), ‘Pixel based digital image forgery detection techniques’, Proc. International Journal of Engineering Research and Applications.
  10. Rajath, B. Suniha, K. (2016), ‘Survey on passive image tampering detection’, Proc. International Advanced Research Science, Engineering and Technology.
  11. Shuguang Zhang, Qiang Zhang, Weidong Min and Yongzhen Ke (2014), ‘Detecting  image forgery based  on noise estimation’,  Proc. International Journal of Multimedia and Ubiquitous Engineering, Vol.9, No.1, pp.325-336.
  12. Siwei Lyu, Xunyu Pan (2010), ‘Region duplication  detection using image feature matching’, Proc. IEEE Transactions  on Information  Forensics and Security.
  13. Zhao, X. Li, J. Li, S. and Wang, S. (2010) ‘Detecting digital image splicing in chroma spaces’, Proc. International workshop on digital watermarking.
  14. Zhenhua, Q. Guoping, Q. and Jiwu, H. (2009) ‘Detect digital image splicing with visual cues’, Proc.International workshop on information hiding.

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 238-242
Manuscript Number : CSEIT172672
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

R Gomathi, "Forged Image Detection by Analyzing Edge, Visual Saliency and Textural Features using SVM Classifier ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.238-242, November-December-2017.
Journal URL :

Article Preview