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

机译:您在说谎吗:验证室外图像的时间位置

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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° and 4.8° 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.
机译:在我们的社会中,照片通常用于传达信息,而相关的上下文信息(即捕获时间和位置)是照片传达的关键部分。但是,伪造者很容易篡改或错误地声明上下文信息,以实现恶意目标,例如在公众中引起恐惧或扭曲公众意见。因此,本文旨在仅使用照片的内容来验证拍摄时间和位置。出于古代人如何通过阴影估算一天中的时间的动机,我们设计了基于射影几何的算法,以利用图像中的阴影估算太阳的位置。同时,我们根据要求的捕获时间和位置,通过应用天文算法来计算太阳位置。通过比较太阳位置的两种估计,我们能够验证捕获时间和位置的一致性,从而验证照片的时间位置。实验结果表明,我们的算法可以估计太阳的位置并检测由于时间,日期和位置纬度的伪造而引起的不一致。通过将太阳位置距离和高度角距离的阈值分别选择为9.2°和4.8°,我们的框架可以正确识别91.1%的正样本,而识别负样本的误差为7.7%请注意,我们假设照片是包含至少一个垂直对象及其阴影。尽管如此,我们认为这项工作是仅使用照片的内容来验证上下文信息的一致性的第一个重要尝试。

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