State-of-the-art presentation attack detection approaches tend to overfit to the presentation attack instruments seen during training and fail to generalize to unknown presentation attack instruments. Given that face presentation attack detection is inherently a local task, we propose a face presentation attack detection framework, namely Self-Supervised Regional Fully Convolutional Network (SSR-FCN), that is trained to learn local discriminative cues from a face image in a self-supervised manner. The proposed framework (i) improves generalizability while maintaining the computational efficiency of holistic face presentation attack detection approaches (<4 ms on a Nvidia GTX 1080Ti GPU), and (ii) is more interpretable since it localizes the parts of the face that are labeled as presentation attacks. Experimental results show that SSR-FCN can achieve TDR = 65% @ 2.0% FDR when evaluated on a dataset, SiW-M, comprising of 13 different presentation attack instruments under unknown attacks while achieving competitive performances under standard benchmark datasets (Oulu-NPU, CASIA-MFSD, and Replay-Attack).

中文翻译：

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局部观察全局推断：一种通用的人脸反欺骗方法


最先进的演示攻击检测方法往往会过度适应训练期间看到的演示攻击工具，并且无法推广到未知的演示攻击工具。鉴于人脸呈现攻击检测本质上是一项本地任务，我们提出了一种人脸呈现攻击检测框架，即自监督区域全卷积网络（SSR-FCN），该框架经过训练，可以从自学习中的人脸图像中学习局部判别线索。 ——监督方式。所提出的框架 (i) 提高了通用性，同时保持了整体人脸呈现攻击检测方法的计算效率（在 Nvidia GTX 1080Ti GPU 上为 <4 毫秒），并且 (ii) 更具可解释性，因为它定位了标记的人脸部分作为演示攻击。实验结果表明，在由 13 种不同的呈现攻击工具组成的数据集 SiW-M 上进行评估时，SSR-FCN 在未知攻击下可以实现 TDR = 65% @ 2.0% FDR，同时在标准基准数据集（Oulu-NPU、 CASIA-MFSD 和重放攻击）。