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Are You Lying: Validating the Time-Location of Outdoor Images

  • Xiaopeng Li
  • Wenyuan Xu
  • Song Wang
  • Xianshan Qu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10355)

Abstract

Photos have been commonly used in our society to convey information, and the associated contextual information (i.e., the capture time and location) is a key part of what a photo conveys. However, the contextual information can be easily tampered or falsely claimed by forgers to achieve malicious goals, e.g., creating fear among the general public or distorting public opinions. Thus, this paper aims at verifying the capture time and location using the content of the photos only. Motivated by how the ancients estimate the time of the day by shadows, we designed algorithms based on projective geometry to estimate the sun position by leveraging shadows in the image. Meanwhile, we compute the sun position by applying astronomical algorithms according to the claimed capture time and location. By comparing the two estimations of the sun position, we are able to validate the consistency of the capture time and location, and hence the time-location of the photos. Experimental results show that our algorithms can estimate sun position and detect the inconsistency caused by falsified time, date, and latitude of location. By choosing the thresholds to be \(9.2^\circ \) and \(4.8^\circ \) for the sun position distance and altitude angle distance respectively, our framework can correctly identify 91.1% of the positive samples, with 7.7% error in identifying the negative samples. Note that we assume that the photos contain at least one vertical object and its shadow. Nevertheless, we believe this work serves as the first and important attempt in verifying the consistency of the contextual information only using the content of the photos.

Keywords

Capture time and location Sun position Shadows Consistency Projective geometry 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xiaopeng Li
    • 1
  • Wenyuan Xu
    • 1
  • Song Wang
    • 1
  • Xianshan Qu
    • 1
  1. 1.Department of CSEUniversity of South CarolinaColumbiaUSA

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