Amplitude SAR Imagery Splicing Localization

• URL: http://arxiv.org/abs/2201.02409v1
• Date: Fri, 7 Jan 2022 11:42:09 GMT
• Title: Amplitude SAR Imagery Splicing Localization
• Authors: Edoardo Daniele Cannas, Nicol\`o Bonettini, Sara Mandelli, Paolo Bestagini, Stefano Tubaro
• Abstract summary: This paper investigates the problem of amplitude SAR imagery splicing localization. We leverage a Convolutional Neural Network (CNN) to extract a fingerprint highlighting inconsistencies in the processing traces of the analyzed input. Results show that our proposed method, tailored to the nature of SAR signals, provides better performances than state-of-the-art forensic tools developed for natural images.
• Score: 17.075910584827568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic Aperture Radar (SAR) images are a valuable asset for a wide variety of tasks. In the last few years, many websites have been offering them for free in the form of easy to manage products, favoring their widespread diffusion and research work in the SAR field. The drawback of these opportunities is that such images might be exposed to forgeries and manipulations by malicious users, raising new concerns about their integrity and trustworthiness. Up to now, the multimedia forensics literature has proposed various techniques to localize manipulations in natural photographs, but the integrity assessment of SAR images was never investigated. This task poses new challenges, since SAR images are generated with a processing chain completely different from that of natural photographs. This implies that many forensics methods developed for natural images are not guaranteed to succeed. In this paper, we investigate the problem of amplitude SAR imagery splicing localization. Our goal is to localize regions of an amplitude SAR image that have been copied and pasted from another image, possibly undergoing some kind of editing in the process. To do so, we leverage a Convolutional Neural Network (CNN) to extract a fingerprint highlighting inconsistencies in the processing traces of the analyzed input. Then, we examine this fingerprint to produce a binary tampering mask indicating the pixel region under splicing attack. Results show that our proposed method, tailored to the nature of SAR signals, provides better performances than state-of-the-art forensic tools developed for natural images.

Related papers

• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities [88.398085358514]
  Contrastive Deepfake Embeddings (CoDE) is a novel embedding space specifically designed for deepfake detection. CoDE is trained via contrastive learning by additionally enforcing global-local similarities.
  arXiv  Detail & Related papers  (2024-07-29T18:00:10Z)
• Hiding Local Manipulations on SAR Images: a Counter-Forensic Attack [17.78894837783128]
  The vast accessibility of Synthetic Aperture Radar (SAR) images through online portals has propelled the research across various fields. Vulnerability is further emphasized by the fact that most SAR products, despite their original complex nature, are often released as amplitude-only information. In this paper we demonstrate that an expert practitioner can exploit the complex nature of SAR data to obscure any signs of manipulation within a locally altered amplitude image.
  arXiv  Detail & Related papers  (2024-07-09T17:03:57Z)
• Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection [86.97062579515833]
  We introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset, comprising samples generated by tft28 distinct generative models. This analysis culminates in the establishment of a novel state-of-the-art performance, showcasing a remarkable tft11.6% improvement over existing methods.
  arXiv  Detail & Related papers  (2023-12-16T14:27:06Z)
• Perceptual Artifacts Localization for Image Synthesis Tasks [59.638307505334076]
  We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. We propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images.
  arXiv  Detail & Related papers  (2023-10-09T10:22:08Z)
• Exposing Image Splicing Traces in Scientific Publications via Uncertainty-guided Refinement [30.698359275889363]
  A surge in scientific publications suspected of image manipulation has led to numerous retractions. Image splicing detection is more challenging due to the lack of reference images and the typically small tampered areas. We propose an Uncertainty-guided Refinement Network (URN) to mitigate the impact of disruptive factors.
  arXiv  Detail & Related papers  (2023-09-28T12:36:12Z)
• SAR Despeckling using a Denoising Diffusion Probabilistic Model [52.25981472415249]
  The presence of speckle degrades the image quality and adversely affects the performance of SAR image understanding applications. We introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method achieves significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods.
  arXiv  Detail & Related papers  (2022-06-09T14:00:26Z)
• Splicing Detection and Localization In Satellite Imagery Using Conditional GANs [26.615687071827576]
  We describe the use of a Conditional Generative Adversarial Network (cGAN) to identify spliced forgeries within satellite images. Our method achieves high success on these detection and localization objectives.
  arXiv  Detail & Related papers  (2022-05-03T22:25:48Z)
• Transformer-based SAR Image Despeckling [53.99620005035804]
  We introduce a transformer-based network for SAR image despeckling. The proposed despeckling network comprises of a transformer-based encoder which allows the network to learn global dependencies between different image regions. Experiments show that the proposed method achieves significant improvements over traditional and convolutional neural network-based despeckling methods.
  arXiv  Detail & Related papers  (2022-01-23T20:09:01Z)
• Fighting Deepfake by Exposing the Convolutional Traces on Images [0.0]
  Mobile apps like FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to produce extreme transformations on human face photos. This kind of media object took the name of Deepfake and raised a new challenge in the multimedia forensics field: the Deepfake detection challenge. In this paper, a new approach aimed to extract a Deepfake fingerprint from images is proposed.
  arXiv  Detail & Related papers  (2020-08-07T08:49:23Z)
• Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks [30.410981386006394]
  despeckling is a crucial preliminary step in scene analysis algorithms. Recent success of deep learning envisions a new generation of despeckling techniques. We propose a self-supervised Bayesian despeckling method.
  arXiv  Detail & Related papers  (2020-07-04T11:38:48Z)

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.