Skip to content

Latest commit

 

History

History
50 lines (39 loc) · 1.86 KB

README.md

File metadata and controls

50 lines (39 loc) · 1.86 KB
Raw

Image splicing detection - python

These are steps to get you started with the project:

  • download Anaconda Navigator, open spyder and run basic "Hello, world!" program. If it doesn't work set environment variables.
  • download Milk Library [from .whl file]
  • download Mahotas Library [from .whl file]
  • download cv2 library [ direct pip ]
  • Copy all code in folder where spyder is set.
  • Download dataset and put them in folders Like negatives and positives.
  • Download glob library to access folders which contains image.
  • Download any package which is not already present in anaconda environment.
  • Rest is simple, code is self explanatory.

Features:

In this project, an improved image splicing detection is purposed which is based on global and local features of an image.

  • Let's get some local features using SIFT which is a local feature extraction method:

SIFT

A robust interest detector SIFT is applied which is tweaked with center of mass algorithm which localizes the spliced object and only nearest points are used concentrically with respect to coordinates of center of mass of given image.

  • Let's get some global features of an image:

Zernike moments

zernike will give measure about how the mass is distributed all over image.

Local binary pattern

Local binary pattern will give measure of how many pixels represent a particular code.

Haralick Features

Haralick Features which is a combination of feature vector which provides 13 useful statistical features.

Methodology:

  • Effective morphology based image filtering techniques are used to reduce the noise and get prominent edge map.
  • Final feature vector by applying PCA which reduces dimention to a fixed component and final feature vector is feeded to SVM classifier for training model.
  • N-fold cross validation is used to get minimally overfitted and accurate model.