earticle

영어

Forgery detection techniques are required to verify the authenticity of the digital images. The additional noise is the most general way to hide the traces of the tampering done to the image. Original images which do not undergo any alterations are supposed to have a consistency in noise variation. If the image is forged, the noise no longer remains consistent throughout the image. In this paper, a method is proposed to detect the forgery based upon noise estimation and hog feature extraction. The image is first converted to YIQ colorspace, and then the block segmentation is performed on Y component of the YIQ image. Noise is estimated using PCA and hog features are extracted from each block of the image. An unsupervised clustering method is used to cluster the blocks of the image. The experimental results show that the proposed technique detects forged images more effectively as compared to previous method based only on noise estimation.