Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results

要約

タイトル: Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results

要約:

– 顔認証システムの偽装対策は重要であるが、実世界での適用に課題が残っている。
– 現存する FAS(顔認証システムの偽装対策) のやり方の限界の一つは、公開されているデータセットがスポーフィングの種類や環境の多様性に欠けていることが原因である。
– これらの問題に対処するために、Wild Face Anti-Spoofing (WFAS) データセットが導入された。詳細は下記の通り。
– WFASデータセットには、321,751人のスポーフィングデータと、148,169人の実在データが含まれ、量的にも質的にも向上しており、17種類の攻撃者によるスポーフィングが含まれている。
– Protocol 1 ( Known-Type)でWFASデータセットを用いて、Wild Face Anti-Spoofing ChallengeがCVPR2023ワークショップにて開催される。Protocol 1とProtocol 2 ( Unknown-Type)でリーダーボードが作成され、リサーチの提案可能性が高まる。
– データセットはInsightfaceで公開される。

要約(オリジナル)

Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the CVPR2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI