MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data
Yonghyun Jeong, Doyeon Kim, Jaehyeon Lee, Minki Hong, Solbi Hwang,, Jongwon Choi

TL;DR
This paper introduces MToFNet, a novel anti-spoofing method leveraging mobile Time-of-Flight data, and presents the large, diverse mToF dataset to improve detection of recaptured spoof images across unseen media.
Contribution
The paper proposes a new anti-spoofing model using paired RGB and depth data, and introduces the first large-scale ToF-based dataset for object anti-spoofing.
Findings
Model achieves robust generalization across unseen domains.
Introduces the largest and most diverse ToF-based anti-spoofing dataset.
Demonstrates effectiveness of ToF data in anti-spoofing tasks.
Abstract
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 moir\'e 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…
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Videos
mToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data· youtube
Taxonomy
TopicsBiometric Identification and Security · Digital Media Forensic Detection · Face recognition and analysis
