Detecting image seam carving with low scaling ratio using multi-scale spatial and spectral entropies

Abstract

Seam carving is the most popular content-aware image retargeting technique. However, it may also be used to correct poor photo composition in photography competition or to remove object from image for malicious purpose. A blind detection approach is presented for seam carved image with low scaling ratio (LSR). It exploits spatial and spectral entropies (SSE) on multi-scale images (candidate image and its down-sampled versions). We observe that when a few seams are deleted from an original image, its SSE distribution is greatly changed. Forty-two features are designed to unveil the statistical properties of SSE in terms of centralized tendency, dispersion tendency and distribution tendency. They are combined with the local binary pattern (LBP)-based energy features to form ninety-six features. Finally, support vector machine (SVM) is exploited as classifier to determine whether an image is original or suffered from seam carving. Experimental results show that the proposed approach achieves superior detection accuracy over the state-of-the-art works, especially for resized image by seam carving with LSRs. Moreover, it is robust against JPEG compression and seam insertion.