FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point Defects

• URL: http://arxiv.org/abs/2107.02016v1
• Date: Mon, 5 Jul 2021 13:35:39 GMT
• Title: FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point Defects
• Authors: Gaojian Wang, Qian Jiang, Xin Jin and Xiaohui Cui
• Abstract summary: 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.
• Score: 9.568679090566262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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