MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data
This addresses the problem of image spoofing in online markets for sellers and platforms, though it appears incremental as it builds on existing anti-spoofing methods with new sensor data.
The paper tackles the problem of detecting spoof images created by recapturing others' images on display screens, which are hard to distinguish visually, by proposing an anti-spoofing method using paired RGB images and depth maps from mobile Time-of-Flight sensors. The result is a model that achieves robust generalization across unseen domains, demonstrated on the newly introduced mToF dataset, the largest and most diverse object anti-spoofing dataset to utilize ToF data.
In online markets, sellers can maliciously recapture others' images on display screens to utilize as spoof images, which can be challenging to distinguish in human eyes. To prevent such harm, we propose an anti-spoofing method using the paired rgb images and depth maps provided by the mobile camera with a Time-of-Fight sensor. When images are recaptured on display screens, various patterns differing by the screens as known as the moiré patterns can be also captured in spoof images. These patterns lead the anti-spoofing model to be overfitted and unable to detect spoof images recaptured on unseen media. To avoid the issue, we build a novel representation model composed of two embedding models, which can be trained without considering the recaptured images. Also, we newly introduce mToF dataset, the largest and most diverse object anti-spoofing dataset, and the first to utilize ToF data. Experimental results confirm that our model achieves robust generalization even across unseen domains.