论文标题
通过分析卷积痕迹来检测深泡剂检测
DeepFake Detection by Analyzing Convolutional Traces
论文作者
论文摘要
由于有可能使用深度学习工具（主要基于临时生成的对抗网络（GAN））创建令人难以置信的逼真图像，因此当今的深层现象已经变得非常流行。在这项工作中，我们着重于对人面部深层效果的分析，目的是创建一种新的检测方法，能够检测图像中隐藏的取证跟踪：图像生成过程中剩下的一种指纹。通过期望最大化（EM）算法，提出的技术提取了一组专门介绍的局部特征，以建模基础卷积生成过程。临时验证是通过对五个不同体系结构（GDWCT，Stargan，Attgan，stylegan，stylegan2）的幼稚分类器进行的实验测试来对Celeba数据集进行的，作为非伪装的基础真相。结果证明了该技术在区分不同架构和相应生成过程中的有效性。
The Deepfake phenomenon has become very popular nowadays thanks to the possibility to create incredibly realistic images using deep learning tools, based mainly on ad-hoc Generative Adversarial Networks (GAN). In this work we focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method able to detect a forensics trace hidden in images: a sort of fingerprint left in the image generation process. The proposed technique, by means of an Expectation Maximization (EM) algorithm, extracts a set of local features specifically addressed to model the underlying convolutional generative process. Ad-hoc validation has been employed through experimental tests with naive classifiers on five different architectures (GDWCT, STARGAN, ATTGAN, STYLEGAN, STYLEGAN2) against the CELEBA dataset as ground-truth for non-fakes. Results demonstrated the effectiveness of the technique in distinguishing the different architectures and the corresponding generation process.
