Validating the Contextual Information of Outdoor Images for Photo Misuse Detection
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Abstract (arXiv)
The contextual information (i.e., the time and location) in which a photo is taken can be easily tampered with or falsely claimed by forgers to achieve malicious purposes, e.g., creating fear among the general public. A rich body of work has focused on detecting photo tampering and manipulation by verifying the integrity of image content. Instead, we aim to detect photo misuse by verifying the capture time and location of photos. This paper is motivated by the law of nature that sun position varies with the time and location, which can be used to determine whether the claimed contextual information corresponds with the sun position that the image content actually indicates. Prior approaches to inferring sun position from images mainly rely on vanishing points associated with at least two shadows, while we propose novel algorithms which utilize only one shadow in the image to infer the sun position. Meanwhile, we compute the sun position by applying astronomical algorithms which take as input the claimed capture time and location. Only when the two estimated sun positions are consistent can the claimed contextual information be genuine. We have developed a prototype called IMAGEGUARD. The experimental results show that our method can successfully estimate sun position and detect the time-location inconsistency with high accuracy. By setting the thresholds to be 9.4 degrees and 5 degrees for the sun position distance and the altitude angle distance, respectively, our system can correctly identify 91.5% of falsified photos with fake contextual information.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Nov 2018.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.
- 2024Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Reviewcited by 3
- 2026GeoContext: One Context Ladder, Two Failure Modes in Vision-Language Geolocation: Flat Reliance on User-Provided Location Context and False Confirmation of Location Claimscited by 0
- 2026GeoContext: One Context Ladder, Two Failure Modes in Vision-Language Geolocation: Flat Reliance on User-Provided Location Context and False Confirmation of Location Claims4 Sep 2026
- 2024Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review23 Feb 2024