论文标题
关于低资源的深泡检测的注释
A Note on Deepfake Detection with Low-Resources
论文作者
论文摘要
Deepfakes是包括更改的视频，通常用神经网络代替具有不同面部的人的面孔。即使该技术作为笑话和模仿的载体获得了知名度，但它通过生物识别模仿或令人垂涎，对自己的安全构成了严重威胁。在本文中，我们提出了两种允许在没有明显计算能力的情况下为用户检测深击的方法。特别是，我们通过替换原始激活功能，可以提高近1％并提高结果的一致性来增强肠系膜。此外，我们介绍并验证了一种新的激活功能 - pish，以少量时间开销的费用可以更高的一致性。 此外，我们基于局部特征描述符（LFD）提供了深击检测方法的初步结果，该方法允许更快地设置系统，而无需诉诸GPU计算。我们的方法达到了相等的错误率为0.28，既准确性又超过0.7。
Deepfakes are videos that include changes, quite often substituting face of a portrayed individual with a different face using neural networks. Even though the technology gained its popularity as a carrier of jokes and parodies it raises a serious threat to ones security - via biometric impersonation or besmearing. In this paper we present two methods that allow detecting Deepfakes for a user without significant computational power. In particular, we enhance MesoNet by replacing the original activation functions allowing a nearly 1% improvement as well as increasing the consistency of the results. Moreover, we introduced and verified a new activation function - Pish that at the cost of slight time overhead allows even higher consistency. Additionally, we present a preliminary results of Deepfake detection method based on Local Feature Descriptors (LFD), that allows setting up the system even faster and without resorting to GPU computation. Our method achieved Equal Error Rate of 0.28, with both accuracy and recall exceeding 0.7.
