Open AccessAn Efficient Forgery Image Detection Method using Hybrid Feature Extraction and Multiclass SVM
Author(s) -
C. Shanthi,
V Raj
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1049.0782s219
Subject(s) - artificial intelligence , pattern recognition (psychology) , principal component analysis , computer science , feature extraction , support vector machine , discrete wavelet transform , wavelet , mathematics , computer vision , wavelet transform
The advancement of image editing software tools in the image processing field has led to an exponential increase in the manipulation of the images. Subjective differentiation of original and manipulated images has become almost impossible. This has kindled the interest among researchers to develop algorithms for detecting the forgery in the image. ImageSplicing, Copy-Move and Image Retouching are the most common image forgery techniques. The existing methods to detect image forgery has drawbacks like false detection, high execution time and low accuracy rate. Considering these issues, this work proposes an efficient method for detection of image forgery. Initially, bilateral filter is used to remove the noise in pre-processing, Chan-Vese Segmentation algorithm is used to detect the clumps from the filtered image utilizing both intensity and edgeinformation, followed by hybrid feature extraction technique. Hybrid feature extraction technique comprises of Dual Tree Complex-Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and Gray-Level-Co-Occurrence Matrix (GLCM). The DWT has dual-tree complex wavelet transform with important properties, it is nearly shift invariant and directionally selective in two and higher dimensions. Principal Component Analysis (PCA) finds the eigenvectors of a covariance matrix with the highest eigenvalues and uses these values to project the data into a new subspace of equal or less dimensions. Gray-Level-Co-Occurrence Matrix (GLCM) extracts the Feature values such as energy, entropy, homogeneity, standard deviation, variance, contrast, correlation and mean. Classification is done based on the texture values of training dataset and testing dataset using Multi Class-Support Vector Machine (SVM). The performance analysis is done based on the True positive, False positive and True negative values. The experimental results obtained using the proposed technique shows a better performance compared to the existing KNN classifier model.