An End-to-End Approach for Seam Carving Detection using Deep Neural Networks
This addresses image security by providing a robust computational tool to detect seam carving alterations, which is important for forensic analysis and authentication.
The paper tackles the problem of detecting seam carving, a content-aware image resizing technique that can be used for tampering, by proposing an end-to-end deep neural network approach that achieves state-of-the-art results, as evidenced by experiments on public and private datasets.
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.