在這個廣泛使用人臉辨識的時代下，人臉偽造攻擊方式也接連著不斷地出現，從最 簡單的照片攻擊、顯示器攻擊在到更先進的 3D 面具攻擊等各種為了突破辨識系統而產 生的人臉偽造攻擊方式，因此，人臉防偽變得更為重要，要如何防範各種攻擊，是研究 者共同的目的，然而許多研究人員會採用不同的感測器，如：近紅外線感測器、結構光 3D 感測器、ToF 感測器、熱感器、遠程光體積描記圖法，來獲取更多特徵，儘管這些方 法往往能達到更高的效能，但是在大多數的場合中，用戶需要使用自己的設備進行身分 認證，而在這種場合中，依賴其他感測器的演算法就無法使用，因此，人臉防偽只能使 用多數消費性電子設備中的彩色鏡頭（例如：智慧型手機、平板電腦、筆記本電腦）。 基於上述考量，本論文研究聚焦於不使用額外感測器前提下，僅使用彩色影像來辨 識人臉真偽，並提出了使用單模式 RGB 影像作為輸入並融合了多幀特徵（光流法、序 列池化）的人臉防偽辨識演算法，並且在架構上選擇能夠學習不同尺度的網路架構以及 增加空間注意力機制，能夠有效阻擋平面攻擊。 本論文在目前最大的人臉防偽辨識資料集 CASIA-SURF CeFA 底下，並且在平均分 類錯誤率(ACER)與真人分類錯誤率(BPCER)分別優於最先進的（SOTA）結果 1.17%與 2.5%，模型不僅克服了未知平面攻擊以及人種多樣性問題，並改善了單模式(彩色影像) 演算法之效能，效能甚至是接近使用多模式影像（彩色、深度及紅外線影像）之演算法。

Nowadays, face recognition systems have been widely deployed in real-life applications, such as Face ID, e-commerce security, and airport security check. Various Presentation Attacks (PAs), such as photo attack, display attack, and 3D Mask attack, have been developed to impersonate other identities, leading face anti-spoofing an increasingly critical issue. Recent years, face anti-spoofing heavily relies specific hardware such as structured-light 3D sensors, Time of Flight (ToF) sensors, Near-infrared (NIR) sensors, thermal sensors, etc. In general, these specific sensors can easily achieve good performance on face anti-spoofing. Yet, for most scenarios, the user simply can rely on limited personal device such as RGB cameras that are embedded in most accessable electronic devices such as smartphone, tablet or laptop. In this study, we proposed a model using single-modal (RGB) with multi-feature, i.e., Optical flow and Rank pooling, fusion network. In addition, The bypass connection and spatial attention are applied to fuse the features from different layers. Experimental results show that the proposed method can achieve state-of-the-art result on the biggest cross-ethnicity face antispoofing dataset CASIA-SURF CeFA with the single-modal (RGB) approach, and closes to the multi-modal (RGB, Depth, IR) state-of-the-art result. The average classification error rate (ACER) and Bona Fide Presentation Classification Error Rate (BPCER) are better than the state-of-the-art results by 1.17% and 2.5%, respectively.

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