  Seam carving is a popular technique for content aware image retargeting. It
can be used to deliberately manipulate images, for example, change the GPS
locations of a building or insert/remove roads in a satellite image. This paper
proposes a novel approach for detecting and localizing seams in such images.
While there are methods to detect seam carving based manipulations, this is the
first time that robust localization and detection of seam carving forgery is
made possible. We also propose a seam localization score (SLS) metric to
evaluate the effectiveness of localization. The proposed method is evaluated
extensively on a large collection of images from different sources,
demonstrating a high level of detection and localization performance across
these datasets. The datasets curated during this work will be released to the
public.
