Forgery detection using noise variance estimation and HOG features - Couverture souple

Walia, Savita; Kaur, Mandeep

 
9786202311458: Forgery detection using noise variance estimation and HOG features
Couverture souple
ISBN 10 : 6202311452 ISBN 13 : 9786202311458
Editeur : Scholars' Press, 2018

Synopsis

In this book, a method is proposed to detect the forgery based upon noise estimation and hog feature extraction on NTSC color image i.e. YIQ color space. The YIQ colorspace is used because it splits the luminance and the color information. For forgery detection, the color information can be discarded as only the variance in the luma component is helpful. The image is first converted to YIQ color space, and then the block segmentation is performed on Y component of the YIQ image. Noise estimation and hog features are extracted from each block of the image. The method used for noise estimation is Principal component analysis (PCA) which estimates the noise as the smallest eigen value of the covariance matrix of the image block. An unsupervised clustering method is used to cluster the blocks of the image based upon noise and hog features combined together. Then, SVM classifier is used for refinement of the clustered blocks. The experimental results show that the proposed technique detects forged images more effectively as compared to previous method which is based only on noise variance estimation.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

In this book, a method is proposed to detect the forgery based upon noise estimation and hog feature extraction on NTSC color image i.e. YIQ color space. The YIQ colorspace is used because it splits the luminance and the color information. For forgery detection, the color information can be discarded as only the variance in the luma component is helpful. The image is first converted to YIQ color space, and then the block segmentation is performed on Y component of the YIQ image. Noise estimation and hog features are extracted from each block of the image. The method used for noise estimation is Principal component analysis (PCA) which estimates the noise as the smallest eigen value of the covariance matrix of the image block. An unsupervised clustering method is used to cluster the blocks of the image based upon noise and hog features combined together. Then, SVM classifier is used for refinement of the clustered blocks. The experimental results show that the proposed technique detects forged images more effectively as compared to previous method which is based only on noise variance estimation.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Scholars' Press
Date d'édition
2018
Langue
anglais
ISBN 10 
6202311452
ISBN 13 
9786202311458
Reliure
Broché
Nombre de pages
72
Coordonnées du fabricant
non disponible
Personne responsable
non disponible