# SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery

arXiv: https://arxiv.org/abs/2108.12534 · HTML: https://mlsanity.com/paper/2108.12534/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 68 references that are in the map.
- First posted on arXiv 28 Aug 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)](https://mlsanity.com/paper/1709.02016/) (2017)
- [Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries](https://mlsanity.com/paper/1903.02495/) (2019)
- [You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery](https://mlsanity.com/paper/1805.09512/) (2018)
- [Detection, Attribution and Localization of GAN Generated Images](https://mlsanity.com/paper/2007.10466/) (2020)
- [Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier](https://mlsanity.com/paper/1802.04881/) (2018)
- [Detection and Localization of Image Forgeries using Resampling Features and Deep Learning](https://mlsanity.com/paper/1707.00433/) (2017)
- [Deep Convolutional Neural Network for Identifying Seam-Carving Forgery](https://mlsanity.com/paper/2007.02393/) (2020)
- [Generative Autoregressive Ensembles for Satellite Imagery Manipulation Detection](https://mlsanity.com/paper/2010.03758/) (2020)
- [Boosting Image Forgery Detection using Resampling Features and Copy-move analysis](https://mlsanity.com/paper/1802.03154/) (2018)
- [Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis](https://mlsanity.com/paper/1803.01711/) (2018)
- [Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices](https://mlsanity.com/paper/2104.05693/) (2021)

## Abstract (arXiv)

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.