Open AccessA Novel Approach for Efficient Forgery Image Detection Using Hybrid Feature Extraction and Classification
Author(s) -
G Clara Shanthi,
V. Cyril Raj
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.27.17879
Subject(s) - artificial intelligence , preprocessor , computer science , support vector machine , pattern recognition (psychology) , feature extraction , image (mathematics) , computer vision , feature (linguistics) , gray level , feature detection (computer vision) , image processing , philosophy , linguistics
Image forgery detection is developing as one of the major research topic among researchers in the area of image forensics. These image forgery detection is addressed by two different types: (i) Active, (ii) Passive. Further consist of some different methods, such as Copy-Move, Image Splicing, and Retouching. Development of the image forgery is very necessary to detect as the image is true or it is forgery. In this paper, an efficient forgery detection and classification technique is proposed by three different stages. At first stage, preprocessing is carried out using bilateral filtering to remove noise. At second stage, extract unique features from forged image by using efficient feature extraction technique namely Gray Level Co-occurance Matrices (GLCM). Here, the GLCM improves the feature extraction accuracy. Finally, forged image is detected by classifying the type of image forgery using Multi Class- Support Vector Machine (SVM). Also, the performance of the proposed method is analyzed using the following metrics: accuracy, sensitivity and specificity.