Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images

• URL: http://arxiv.org/abs/2206.10737v1
• Date: Tue, 21 Jun 2022 21:28:40 GMT
• Title: Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images
• Authors: Benjamin Hadwiger, Christian Riess
• Abstract summary: We explore an alternative approach to splicing detection, which is potentially better suited for images in-the-wild. We learn a deep metric space that is on one hand sensitive to illumination color and camera white-point estimation, but on the other hand insensitive to variations in object color. In our evaluation, we show that the proposed embedding space outperforms the state of the art on images that have been subject to strong compression and downsampling.
• Score: 10.091921099426294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One common task in image forensics is to detect spliced images, where multiple source images are composed to one output image. Most of the currently best performing splicing detectors leverage high-frequency artifacts. However, after an image underwent strong compression, most of the high frequency artifacts are not available anymore. In this work, we explore an alternative approach to splicing detection, which is potentially better suited for images in-the-wild, subject to strong compression and downsampling. Our proposal is to model the color formation of an image. The color formation largely depends on variations at the scale of scene objects, and is hence much less dependent on high-frequency artifacts. We learn a deep metric space that is on one hand sensitive to illumination color and camera white-point estimation, but on the other hand insensitive to variations in object color. Large distances in the embedding space indicate that two image regions either stem from different scenes or different cameras. In our evaluation, we show that the proposed embedding space outperforms the state of the art on images that have been subject to strong compression and downsampling. We confirm in two further experiments the dual nature of the metric space, namely to both characterize the acquisition camera and the scene illuminant color. As such, this work resides at the intersection of physics-based and statistical forensics with benefits from both sides.

Related papers

• Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures [34.8728594246521]
  We describe a perceptual CD measure based on the multiscale sliced Wasserstein distance. Experimental results indicate that our CD measure performs favorably in assessing CDs in photographic images. Our measure functions as a metric in the mathematical sense, and show its promise as a loss function for image and video color transfer tasks.
  arXiv  Detail & Related papers  (2024-07-14T12:48:16Z)
• Multiple Latent Space Mapping for Compressed Dark Image Enhancement [51.112925890246444]
  Existing dark image enhancement methods take compressed dark images as inputs and achieve great performance. We propose a novel latent mapping network based on variational auto-encoder (VAE) Comprehensive experiments demonstrate that the proposed method achieves state-of-the-art performance in compressed dark image enhancement.
  arXiv  Detail & Related papers  (2024-03-12T13:05:51Z)
• SUCRe: Leveraging Scene Structure for Underwater Color Restoration [1.9490160607392462]
  We introduce SUCRe, a novel method that exploits the scene's 3D structure for underwater color restoration. We conduct extensive quantitative and qualitative analyses of our approach in a variety of scenarios ranging from natural light to deep-sea environments.
  arXiv  Detail & Related papers  (2022-12-18T16:53:13Z)
• Detecting Recolored Image by Spatial Correlation [60.08643417333974]
  Image recoloring is an emerging editing technique that can manipulate the color values of an image to give it a new style. In this paper, we explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring. Our method achieves the state-of-the-art detection accuracy on multiple benchmark datasets and exhibits well generalization for unknown types of recoloring methods.
  arXiv  Detail & Related papers  (2022-04-23T01:54:06Z)
• ObjectFormer for Image Manipulation Detection and Localization [118.89882740099137]
  We propose ObjectFormer to detect and localize image manipulations. We extract high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-03-28T12:27:34Z)
• Astronomical Image Colorization and upscaling with Generative Adversarial Networks [0.0]
  This research aims to provide an automated approach for the problem by focusing on a very specific domain of images, namely astronomical images. We explore the usage of various models in two different color spaces, RGB and L*a*b. The model produces visually appealing images which hallucinate high resolution, colorized data in these results which does not exist in the original image.
  arXiv  Detail & Related papers  (2021-12-27T19:01:20Z)
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding [88.46682991985907]
  We present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding.
  arXiv  Detail & Related papers  (2021-04-27T07:35:30Z)
• The color out of space: learning self-supervised representations for Earth Observation imagery [10.019106184219515]
  We propose to learn meaningful representations from satellite imagery, leveraging its high-dimensionality spectral bands to reconstruct visible colors. We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor.
  arXiv  Detail & Related papers  (2020-06-22T10:21:36Z)
• Instance-aware Image Colorization [51.12040118366072]
  In this paper, we propose a method for achieving instance-aware colorization. Our network architecture leverages an off-the-shelf object detector to obtain cropped object images. We use a similar network to extract the full-image features and apply a fusion module to predict the final colors.
  arXiv  Detail & Related papers  (2020-05-21T17:59:23Z)
• Learning to Structure an Image with Few Colors [59.34619548026885]
  We propose a color quantization network, ColorCNN, which learns to structure the images from the classification loss in an end-to-end manner. With only a 1-bit color space (i.e., two colors), the proposed network achieves 82.1% top-1 accuracy on the CIFAR10 dataset. For applications, when encoded with PNG, the proposed color quantization shows superiority over other image compression methods in the extremely low bit-rate regime.
  arXiv  Detail & Related papers  (2020-03-17T17:56:15Z)

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.