论文标题
用于深泡检测的机器学习方法
A Machine Learning Approach for DeepFake Detection
论文作者
论文摘要
随着DeepFake技术的传播，这项技术变得非常易于访问和足够好，以至于对其恶意使用感到担忧。面对这个问题，检测伪造的面孔对于确保安全并避免在全球和私人规模上避免社会政治问题至关重要。本文提出了一种使用卷积神经网络检测深击的解决方案，并为此目的开发了一个数据集-Celeb -DF。结果表明，在这些图像的分类中，总体准确性为95％，提出的模型接近了最新的现状，并且可以调整可能会在未来出现的操纵技术中进行更好的结果。
With the spread of DeepFake techniques, this technology has become quite accessible and good enough that there is concern about its malicious use. Faced with this problem, detecting forged faces is of utmost importance to ensure security and avoid socio-political problems, both on a global and private scale. This paper presents a solution for the detection of DeepFakes using convolution neural networks and a dataset developed for this purpose - Celeb-DF. The results show that, with an overall accuracy of 95% in the classification of these images, the proposed model is close to what exists in the state of the art with the possibility of adjustment for better results in the manipulation techniques that arise in the future.
