Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Key-dependant decomposition based image watermarking
Proceedings of the 12th annual ACM international conference on Multimedia
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Improved seam carving for video retargeting
ACM SIGGRAPH 2008 papers
Optimized scale-and-stretch for image resizing
ACM SIGGRAPH Asia 2008 papers
Digital Image Watermarking: An Overview
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Detection of seam carving and localization of seam insertions in digital images
Proceedings of the 11th ACM workshop on Multimedia and security
Optimized image resizing using seam carving and scaling
ACM SIGGRAPH Asia 2009 papers
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
A comparative study of image retargeting
ACM SIGGRAPH Asia 2010 papers
Seam carving estimation using forensic hash
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
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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 2x2 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% and 95.8% for 20% and 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.