Welcome to the InfoSci Platform
Reference Hub5
Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

Gulivindala Suresh, Chanamallu Srinivasa Rao
Copyright: © 2020 |Volume: 12 |Issue: 3 |Pages: 18
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781799805816|DOI: 10.4018/IJDCF.2020070103
Cite Article Cite Article

MLA

Suresh, Gulivindala, and Chanamallu Srinivasa Rao. "Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features." IJDCF vol.12, no.3 2020: pp.27-44. http://doi.org/10.4018/IJDCF.2020070103

APA

Suresh, G. & Rao, C. S. (2020). Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features. International Journal of Digital Crime and Forensics (IJDCF), 12(3), 27-44. http://doi.org/10.4018/IJDCF.2020070103

Chicago

Suresh, Gulivindala, and Chanamallu Srinivasa Rao. "Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features," International Journal of Digital Crime and Forensics (IJDCF) 12, no.3: 27-44. http://doi.org/10.4018/IJDCF.2020070103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.