Image Forgery Detection based on Expectation Maximization Algorithm

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Description

Image Forgery Detection based on Expectation Maximization Algorithm

Abstract

In this project, forgery is vital as the usage of digital pictures and images in several important places such as courts or hospitals, can misuse the technology and use it against the accused in order to get away from the crime. With the development of high-resolution digital cameras and photographs the probability of images and pictures being forged is high as the photographers have the ability to manipulate and forge the images as they desire. This can lead to a misuse of technology as forgery can lead to a major crime or difference in important and critical situations or places such as courts, hospitals, and also education system. Proposed techniques supported SIFT feature to sight the native feature of the image and effective agglomeration. This project is planned to analyze a digital image by mistreatment EM rule. A sample library of the cast and unmodified pictures, including a variety of pictures from the Associate in Nursing Multi meddling dataset.


System Analysis

   Existing Systems

  • Error level analysis detection technique
  • Colour filter array detection technique

Drawbacks of Exisitng System

  • High Computational time.
  • Less accuracy in classification
  •  Poor Edge detection.
  • Less accuracy 
  • It is not suitable for all lighting conditions images

    Proposed Method

    • DWT 
    • Preprocessing 
    • Feature matching
    • Expectation maximization algorithm

    Advantages

    • The segmentation algorithm Proves to be simple and effective
    • The greyscale Co-occurrence matrix performed well in NN
    • Better texture and edge representation 
    • Segmentation provides better clustering efficiency

    Block Diagram 

    Image Forgery Detection based on Expectation Maximization Algorithm 2
    Image Forgery Detection Based on Expectation-Maximization Algorithm

    Hardware Requirements

    • system
    • 4 GB of RAM
    • 500 GB of Hard disk

    Software Requirement

    • MATLAB 2014a

    REFERENCES

     [1] Wei Luo, Jiwu Huang, GuopingQiu, “Robust Detection of RegionDuplication Forgery in Digital Image”, 18th IEEE International Conference on Pattern Recognition, Hong Kong, p. 746 – 749, 2006. 

    [2] Xiaobing KANG, ShengMin WEI, “Identifying Tampered Regions Using Singular Value Decomposition in Digital Image Forensics”, IEEE International Conference on Computer Science and Software Engineering, Wuhan, Hubei, p. 926 – 930, 2008. 

    [3] Alin C Popescu and Hany Farid, “Exposing Digital Forgeries by Detecting Duplicated Image Regions”, Dartmouth Computer Science Technical Report TR2004-515, USA, August 2004. 

    [4] Hwei-Jen Lin, Chun-Wei Wang, And Yang-Ta Kao, “Fast Copy-Move Forgery Detection”, WSEAS Transactions on Signal Processing, p. 188- 197, May 2009. 

    [5] HieuCuong Nguyen and Stefan Katzenbeisser, “Detection of copy-move forgery in digital images using Radon transformation and phase correlation”, IEEE Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Piraeu, 2012. 

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