SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery
원문 초록
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.
함께 살펴볼 논문
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Prune and Repaint: Content-Aware Image Retargeting for any Ratio
Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions
OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality Assessment
Self-Play Reinforcement Learning for Fast Image Retargeting
Cycle-IR: Deep Cyclic Image Retargeting
HALO: Human-Aligned End-to-end Image Retargeting with Layered Transformations
토론
이 논문과 연결된 포럼 글이 아직 없습니다.