The internet is filled with fake face images and videos synthesized by deep generative models. These realistic DeepFakes pose a challenge to determine the authenticity of multimedia content. As countermeasures, artifact-based detection methods suffer from insufficiently fine-grained features that lead to limited detection performance. DNN-based detection methods are not efficient enough, given that a DeepFake can be created easily by mobile apps and DNN-based models require high computational resources. We show that DeepFake faces have fewer feature points than real ones, especially in certain facial regions. Inspired by feature point detector-descriptors to extract discriminative features at the pixel level, we propose the Fused Facial Region_Feature Descriptor (FFR_FD) for effective and fast DeepFake detection. FFR_FD is only a vector extracted from the face, and it can be constructed from any feature point detector-descriptors. We train a random forest classifier with FFR_FD and conduct extensive experiments on six large-scale DeepFake datasets, whose results demonstrate that our method is superior to most state of the art DNN-based models.
翻译:互联网上充满了由深基因模型合成的假面相和视频。 这些现实的深面图象对确定多媒体内容的真实性提出了挑战。 作为对策,基于文物的探测方法缺乏导致检测性能有限的微细特征。 基于 DNN 的检测方法不够有效, 因为在移动应用程序和基于 DNN 的模型中, 深面图象可以很容易生成, 需要高计算资源。 我们显示, DeepFake 脸孔的特征点比真实的要小, 特别是在某些面部区域。 受地貌点探测器描述器的启发, 要在像素水平上提取歧视性特征。 作为对策, 我们建议使用 FUR_ FD 来有效和快速地检测 FUD 。 FFR_ FD 仅仅是从脸部提取的矢量, 并且可以用任何地点探测器来构建。 我们用 FFR_ FDD 随机的森林分类器, 并在六种大型深Fake 数据集上进行广泛的实验, 其结果显示我们的方法优于基于 DNN 的艺术模型的多数状态 。