SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery
Chandrakanth Gudavalli, Erik Rosten, Lakshmanan Nataraj, Shivkumar Chandrasekaran, Bangalore S Manjunath · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · 2022
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 displace/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 code and datasets curated during this work will be released to the public.