人臉防偽辨識對於人臉辨識系統的安全性至關重要，已經有許多基於卷積神經網路的防偽方法被提出，而且這些方法都表現出了良好的性能。儘管取得了這些成功，但卷積神經網路對於對抗樣本的脆弱性讓這些防偽方法容易受到攻擊。

在這項研究中，對抗性雜訊被用於為幾種防偽方法製作對抗樣本，多種攻擊方法被應用於多幀防偽方法以研究其脆弱性。我們證明即便是傳統方法也有機會可以成功攻擊這些防偽模型。然而，在一些困難的攻擊對象如多幀防偽模型，以這些方法所生成的攻擊圖片在攻擊過程中通常會嚴重失真且容易被人眼識別。由於需要大量的雜訊來欺騙模型，這些雜訊方法可能不適合用於攻擊多幀防偽模型。因此，我們提出了一種新的智能展示攻擊以生成變形圖片來有效地攻擊反偽方法。

我們方法的泛化性透過對四個公開可使用的數據集進行多次實驗來驗證，實驗結果證實我們的方法表現良好，並且使用該方法所產生的攻擊圖片比使用傳統雜訊方法的圖片失真更少。此外，我們證明了使用元學習訓練並具有較強域泛化能力的防偽模型也容易受到這些攻擊。

Face anti-spoofing critically contributes to the security of face recognition systems. Numerous anti-spoofing approaches based on convolutional neural networks (CNNs) have been proposed, and they have shown promising performance. Despite these successes, the vulnerability of CNNs to adversarial examples leaves these anti-spoofing methods vulnerable to attacks.

In this study, adversarial noise is used to craft adversarial examples for several anti-spoofing methods, and various attack methods are applied to investigate the vulnerability of the multi-frame anti-spoofing approach. We demonstrate that even conventional methods can possibly attack these anti-spoofing models successfully. However, in some difficult attack targets such as a multi-frame anti-spoofing model, the attack image generated by these methods is typically heavily distorted during the attack process and can easily be distinguished by human eyes. Because a large amount of noise is needed to deceive the model, these noise methods may be unsuitable for attacking a multi-frame anti-spoofing model. Thus, we proposed a new intelligent face presentation attack approach to generate morph images to effectively attack anti-spoofing approaches.

The generalization of our methodology was validated through several experiments on four publicly available datasets. The experimental results showed that the proposed method performed promisingly, and the attack images generated using this method appeared less distorted than those produced using conventional noise methods. Moreover, we demonstrated that an anti-spoofing model trained using meta-learning and having strong domain generalizability is also vulnerable to these attacks.

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