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SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery
Computer Vision and Pattern Recognition · Submitted 28 Aug 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Seam carving is a popular technique for content aware image retargeting. | background 1.00 | 0.03 | 0.02 | asserted 1.00 |
| s1 | 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. | background 1.00 | 0.05 | 0.02 | hedged possibility 0.81 |
| s2 | This paper proposes a novel approach for detecting and localizing seams in such images. | contribution 1.00 | 0.80 | 0.16 | asserted 0.99 |
| s3 | 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. | gap 0.62 | 0.77 | 0.35 | asserted 0.99 |
| s4 | We also propose a seam localization score (SLS) metric to evaluate the effectiveness of localization. | contribution 0.93 | 0.95 | 0.07 | asserted 0.99 |
| s5 | 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. | result 0.99 | 0.96 | 0.60 | asserted 1.00 |
| s6 | The datasets curated during this work will be released to the public. | contribution 0.70 | 0.75 | 0.03 | asserted 0.99 |
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The paper
Original abstract
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.
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Cites as support 2 papers
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Furthermore, it has been observed that many of these techniques do not perform well when naively applied to overhead images [ref] .
You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery · Related Work · 0.97
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Furthermore, it has been observed that many of these techniques do not perform well when naively applied to overhead images [ref] .
Generative Autoregressive Ensembles for Satellite Imagery Manipulation Detection · Related Work · 0.97
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