论文标题
MRI-GAN：一种使用感知图像评估检测深击的广义方法
MRI-GAN: A Generalized Approach to Detect DeepFakes using Perceptual Image Assessment
论文作者
论文摘要
Deepfakes是通过与其他人的脸交换原始图像的面孔来产生的合成视频。在本文中，我们描述了我们的工作，以开发一般的，基于深度学习的模型来对深层含量进行分类。我们提出了一个新的框架，用于使用基于生成的对抗网络（GAN）的模型，我们称MRI-GAN使用图像中的感知差异来检测合成视频。我们使用DeepFake检测挑战数据集测试了MRI-GAN方法和基于纯框架的模型。我们的基于普通框架的模型可实现91％的测试精度，并使用我们的MRI-GAN框架进行结构相似性指数测量（SSIM），以实现感知差异达到74％的测试准确性。 MRI-GAN的结果是初步的，可以通过修改损失函数，调整超参数或使用更高级的感知相似性度量来进一步改善。
DeepFakes are synthetic videos generated by swapping a face of an original image with the face of somebody else. In this paper, we describe our work to develop general, deep learning-based models to classify DeepFake content. We propose a novel framework for using Generative Adversarial Network (GAN)-based models, we call MRI-GAN, that utilizes perceptual differences in images to detect synthesized videos. We test our MRI-GAN approach and a plain-frames-based model using the DeepFake Detection Challenge Dataset. Our plain frames-based-model achieves 91% test accuracy and a model which uses our MRI-GAN framework with Structural Similarity Index Measurement (SSIM) for the perceptual differences achieves 74% test accuracy. The results of MRI-GAN are preliminary and may be improved further by modifying the choice of loss function, tuning hyper-parameters, or by using a more advanced perceptual similarity metric.
