  Seam carving is a computational method capable of resizing images for both
reduction and expansion based on its content, instead of the image geometry.
Although the technique is mostly employed to deal with redundant information,
i.e., regions composed of pixels with similar intensity, it can also be used
for tampering images by inserting or removing relevant objects. Therefore,
detecting such a process is of extreme importance regarding the image security
domain. However, recognizing seam-carved images does not represent a
straightforward task even for human eyes, and robust computation tools capable
of identifying such alterations are very desirable. In this paper, we propose
an end-to-end approach to cope with the problem of automatic seam carving
detection that can obtain state-of-the-art results. Experiments conducted over
public and private datasets with several tampering configurations evidence the
suitability of the proposed model.
