MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data

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 data. Experimental results confirm that our model achieves robust generalization even across unseen domains.