DoFNet: Depth of Field Difference Learning for Detecting Image Forgery
| Autor: | Changhyun Park, Jongwon Choi, Sehyeon Park, Seungjai Min, Youngjune Gwon, Minki Hong, Yonghyun Jeong, Doyeon Kim |
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| Rok vydání: | 2021 |
| Předmět: |
Bank account
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Real image Insurance claims restrict 0202 electrical engineering electronic engineering information engineering Image forgery 020201 artificial intelligence & image processing Computer vision Depth of field Artificial intelligence business |
| Zdroj: | Computer Vision – ACCV 2020 ISBN: 9783030695439 ACCV (6) |
| Popis: | Recently, online transactions have had an exponential growth and expanded to various cases, such as opening bank accounts and filing for insurance claims. Despite the effort of many companies requiring their own mobile applications to capture images for online transactions, it is difficult to restrict users from taking a picture of other’s images displayed on a screen. To detect such cases, we propose a novel approach using paired images with different depth of field (DoF) for distinguishing the real images and the display images. Also, we introduce a new dataset containing 2,752 pairs of images capturing real and display objects on various types of displays, which is the largest real dataset employing DoF with multi-focus. Furthermore, we develop a new framework to concentrate on the difference of DoF in paired images, while avoiding learning individual display artifacts. Since DoF lies on the optical fundamentals, the framework can be widely utilized with any camera, and its performance shows at least \(23\%\) improvement compared to the conventional classification models. |
| Databáze: | OpenAIRE |
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