Deep learning has been successfully appertained to solve various complex problems in the area of big data analytics to computer vision. A deep learning-powered application recently emerged is Deep Fake. It helps to create fake images and videos that human cannot distinguish them from the real ones and are recent off-shelf manipulation technique that allows swapping two identities in a single video. Technology is a controversial technology with many wide-reaching issues impacting society. So, to counter this emerging problem, we introduce a dataset of 140k real and fake faces which contain 70k real faces from the Flickr dataset collected by Nvidia, as well as 70k fake faces sampled from 1 million fake faces generated by style GAN. We will train our model in the dataset so that our model can identify real or fake faces.
翻译:深度学习成功地解决了计算机视觉大数据分析领域的各种复杂问题。 最近出现了一个深层次的学习动力应用程序“深假 ” 。 它有助于制作假图像和视频,人类无法将其与真实的图像和视频区分开来,而这是最近的一种非现货操作技术,可以将两个身份转换成一个视频。 技术是一个有争议的技术,影响社会的问题很多。 因此,为了应对这个正在出现的问题,我们引入了一个由140k真实和假面孔组成的数据集,其中包含Nvidia收集的Flickr数据集的70k个真实面孔,以及从System GAN生成的100万个假面孔中抽样的70k假面孔。 我们将在数据集中培训我们的模型,以便我们的模型能够识别真实或假面孔。