A patch analysis method to detect seam carved images

•We propose a patch analysis approach to detecting seam-carved images.•Images are divided into 2 × 2 blocks and the optimal patch type for each block is selected.•Analyzing the patch transition probability achieves currently best detection accuracy.•This method can be extended to identify the hot regions frequently crossed by carved seams.

Seam carving is a content-aware image processing algorithm that has been successfully applied to resizing and deliberately removing objects from digital images. Retargeting images by seam carving is hard to identify; therefore, the detection of seam-carved images has been an important and attractive research topic. Existing methods for detecting seam-carved images include those derived from steganography attacks and those based on statistical features. However, these algorithms leave scope for further improvement. Here, we propose a novel method in which images are divided into 2×22×2 blocks, referred to as mini-squares  , and then searched for one of nine types of patches that is likely to recover a mini-square from seam carving. Our method analyzes the patch transition probability among three-connected mini-squares and achieves currently best detection accuracies, namely, 92.2%92.2% and 95.8%95.8% for 20%20% and 50%50% seam-carved images respectively. We also discuss in this paper other potential applications of our patch analysis method, for example, identification of the hot regions frequently crossed by carved seams.