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

• URL: http://arxiv.org/abs/2108.12534v1
• Date: Sat, 28 Aug 2021 00:00:37 GMT
• Title: SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery
• Authors: Chandrakanth Gudavalli, Erik Rosten, Lakshmanan Nataraj, Shivkumar Chandrasekaran, B. S. Manjunath
• Abstract summary: Seam carving is a popular technique for content aware image manipulation. This paper proposes a novel approach for detecting and localizing seams in such images.
• Score: 15.127101376238418
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization [17.270110456445806]
  TruFor is a forensic framework that can be applied to a large variety of image manipulation methods. We rely on the extraction of both high-level and low-level traces through a transformer-based fusion architecture. Our method is able to reliably detect and localize both cheapfakes and deepfakes manipulations outperforming state-of-the-art works.
  arXiv  Detail & Related papers  (2022-12-21T11:49:43Z)
• Visual Cross-View Metric Localization with Dense Uncertainty Estimates [11.76638109321532]
  This work addresses visual cross-view metric localization for outdoor robotics. Given a ground-level color image and a satellite patch that contains the local surroundings, the task is to identify the location of the ground camera within the satellite patch. We devise a novel network architecture with denser satellite descriptors, similarity matching at the bottleneck, and a dense spatial distribution as output to capture multi-modal localization ambiguities.
  arXiv  Detail & Related papers  (2022-08-17T20:12:23Z)
• LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity [49.84167231111667]
  Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image. We introduce an approach to enhance the learning of dense equivariant representations in a self-supervised fashion. We show that having such a prior in the feature extractor helps in landmark detection, even under drastically limited number of annotations.
  arXiv  Detail & Related papers  (2022-04-06T17:48:18Z)
• Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching [102.39635336450262]
  We address the problem of ground-to-satellite image geo-localization by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image.
  arXiv  Detail & Related papers  (2022-03-26T20:10:38Z)
• Seam Carving Detection and Localization using Two-Stage Deep Neural Networks [15.31182286085455]
  We propose a two-step method to detect and localize seam carved images. First, we build a detector to detect small patches in an image that has been seam carved. Next, we compute a heatmap on an image based on the patch detector's output.
  arXiv  Detail & Related papers  (2021-09-04T01:49:08Z)
• Deep Metric Learning for Ground Images [4.864819846886142]
  We deal with the initial localization task, in which we have no prior knowledge about the current robot positioning. We propose a deep metric learning approach that retrieves the most similar reference images to the query image. In contrast to existing approaches to image retrieval for ground images, our approach achieves significantly better recall performance and improves the localization performance of a state-of-the-art ground texture based localization method.
  arXiv  Detail & Related papers  (2021-09-03T14:43:59Z)
• Content-Based Detection of Temporal Metadata Manipulation [91.34308819261905]
  We propose an end-to-end approach to verify whether the purported time of capture of an image is consistent with its content and geographic location. The central idea is the use of supervised consistency verification, in which we predict the probability that the image content, capture time, and geographical location are consistent. Our approach improves upon previous work on a large benchmark dataset, increasing the classification accuracy from 59.03% to 81.07%.
  arXiv  Detail & Related papers  (2021-03-08T13:16:19Z)
• Instance Localization for Self-supervised Detection Pretraining [68.24102560821623]
  We propose a new self-supervised pretext task, called instance localization. We show that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning. Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection.
  arXiv  Detail & Related papers  (2021-02-16T17:58:57Z)
• Self-supervised Segmentation via Background Inpainting [96.10971980098196]
  We introduce a self-supervised detection and segmentation approach that can work with single images captured by a potentially moving camera. We exploit a self-supervised loss function that we exploit to train a proposal-based segmentation network. We apply our method to human detection and segmentation in images that visually depart from those of standard benchmarks and outperform existing self-supervised methods.
  arXiv  Detail & Related papers  (2020-11-11T08:34:40Z)
• Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps [78.2581910688094]
  This work introduces a novel self-enhancement method to harvest accurate object localization maps and object boundaries with only category labels as supervision. In particular, the proposed Self-Enhancement Maps achieve the state-of-the-art localization accuracy of 54.88% on ILSVRC.
  arXiv  Detail & Related papers  (2020-06-09T12:35:55Z)
• Automatic Signboard Detection and Localization in Densely Populated Developing Cities [0.0]
  Signboard detection in natural scene images is the foremost task for error-free information retrieval. We present a novel object detection approach that can detect signboards automatically and is suitable for such cities. Our proposed method can detect signboards accurately (even if the images contain multiple signboards with diverse shapes and colours in a noisy background) achieving 0.90 mAP (mean average precision)
  arXiv  Detail & Related papers  (2020-03-04T08:04:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.