An End-to-End Approach for Seam Carving Detection using Deep Neural Networks

TL;DR

This paper presents a deep neural network-based end-to-end method for detecting seam carving in images, addressing a challenging problem in image forensics with high accuracy.

Contribution

It introduces a novel deep learning approach specifically designed for automatic seam carving detection, achieving state-of-the-art performance.

Findings

High detection accuracy on public datasets

Effective across various tampering configurations

Robustness demonstrated on private datasets

Abstract

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.