Identification of various image retargeting techniques using hybrid features

Abstract

Image retargeting refers to manipulating an input image to adapt its display on diverse screens with distinct spatial resolutions and aspect ratios. However, it might also be used for malicious image forgeries such as object removal. There exist several types of image retargeting techniques, yet most existing image retargeting detection approaches expose only single type of image retargeting. This paper proposes a blind forensics approach to identify three image retargeting techniques including seam carving, scaling and scale-and-stretch. Since image retargeting usually leads to local texture distortion and global structure deformation, a 512-dimensional feature set, which is made up of LBPCO (co-occurrence of local binary pattern) and BSIF (binarized statistical image features), is designed to expose image retargeting artifacts. Support vector machine (SVM) is exploited as pattern classifier to identify three image retargeting techniques. Compared with existing works, the proposed approach not only detects image retargeting forgery, but also identifies specific image retargeting technique.