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

arXiv: https://arxiv.org/abs/2203.02728 · HTML: https://mlsanity.com/paper/2203.02728/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 5 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Deep Convolutional Neural Network for Identifying Seam-Carving Forgery](https://mlsanity.com/paper/2007.02393/) (2020)
- [Seam Carving Detection and Localization using Two-Stage Deep Neural Networks](https://mlsanity.com/paper/2109.01764/) (2021)

## Built on by (most cited first, 1 of 1)

- [Retargeting Visual Data with Deformation Fields](https://mlsanity.com/paper/2311.13297/) (2023)

## Abstract (arXiv)

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.