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研究生： 金名捷
Chin, Ming-Chieh
論文名稱： 基於強烈輔助預測以及有區別的特徵學習進行人臉反偽造偵測
Face Anti-Spoofing via Robust Auxiliary Estimation and Discriminative Feature Learning
指導教授： 許秋婷
Hsu, Chiou-Ting
口試委員: 王聖智
Wang, Sheng-Jyh
邵皓強
Shao, Hao-Chiang
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年： 2021
畢業學年度： 109
語文別： 英文
論文頁數： 32
中文關鍵詞： 人臉反偽造 、對抗學習 、對比損失
外文關鍵詞： Face anti-spoofing, Adversarial learning, Contrastive loss
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    • 人臉反偽造對於依賴偵測臉部真實性的應用程式至關重要。最近輔助資訊如臉部深度圖和 rPPG 訊號，已被成功地應用以提高臉部部反偽造的性能。因此，輔助資訊的預測品質是分類真實/偽造有效性的關鍵。在本文中，我們關注輔助訊息之預測的魯棒性和潛在特徵的可辨別性。我們提出在對抗性學習框架中估計輔助資訊以真實/偽造分類器的訓練。我們在對比損失中加入了額外的約束，並提出了一個區別性的批量對比損失來學習潛在特徵。輔助資訊和區別性潛在特徵都用於分類真實/欺騙。此外，由於並非所有輔助監督都同樣可靠，因此我們提出了一種適應融合策略來融合來自不同輔助監督分支的預設結果。在數個基準資料集上的實驗結果表明，提出的方法明顯優於以前的方法。

    Face anti-spoofing is critical to applications which heavily rely on the authenticity of detected faces. Recently, auxiliary information, such as facial depth maps and rPPG signals, have been successfully included to boost the performance of face anti-spoofing. Consequently, the quality of auxiliary estimation is key to the effectiveness of live/spoof classification. In this thesis, we focus on the robustness of auxiliary estimation and the discriminability of latent features. We propose to estimate the auxiliary information along with the training of live/spoof classifier in an adversarial learning framework. We include additional constraints in the contrastive loss and propose a discriminative batch-contrastive loss to learn the latent features. Both the auxiliary information and the discriminative latent features are included into the live/spoof classification. In addition, because not all the auxiliary supervisions are equally reliable, we propose an adaptive fusing strategy to fuse the estimation results from different auxiliary-supervised branches. Experimental results on several benchmark datasets show that the proposed method significantly outperforms previous methods.

    摘要 i Abstract ii Acknowledgements 1. Introduction 1 2. Related Work 4 2.1 Texture­based Methods 4 2.2 Depth­based Methods 5 2.3 Temporal­based Methods 5 3. Method 7 3.1 Auxiliary Information 8 3.1.1 Facial Depth Map 9 3.1.2 Facial rPPG Map 9 3.2 Network Architecture of Single Branch 10 3.3 Auxiliary Estimation and Discriminative Feature Learning 10 3.3.1 Auxiliary Estimation 11 3.3.2 Adversarial Training 11 3.3.3 Discriminative Batch­Contrastive Loss 12 3.3.4 Total Loss 13 3.4 Live/Spoof Classification 14 3.4.1 Single Branch Detection 14 3.4.2 Detection by Adaptive Fusion 15 4. Experiments 17 4.1 Overview 17 4.2 Dataset 17 4.2.1 OULU­NPU 17 4.2.2 SiW 18 4.2.3 CASIA­MFSD 18 4.2.4 Replay­Attack 18 4.3 Evaluation metrics 19 4.4 Implementation Details 19 4.5 Network Structure 19 4.6 Ablation Study 22 4.6.1 Comparison between Different Contrastive Loss Terms 22 4.6.2 Comparison between Different Losses and Detection Scores 23 4.6.3 Adaptive Fusion Strategy 24 4.6.4 Intra Testing and Cross Testing 25 5. Conclusion 29 References 30

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