Netinfo Security ›› 2022, Vol. 22 ›› Issue (11): 77-84.doi: 10.3969/j.issn.1671-1122.2022.11.010

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Deepfake Detection Algorithm Based on Image Fine-Grained Features

PENG Shufan, CAI Manchun(), LIU Xiaowen, MA Rui   

  1. College of Information Network Security, People’s Public Security University of China, Beijing 100038, China
  • Received:2022-06-30 Online:2022-11-10 Published:2022-11-16
  • Contact: CAI Manchun E-mail:caimanchun@ppsuc.edu.cn

Abstract:

With the development of deep learning, deepfake generation models have overcome the drawback of having obvious artifacts in the generated images, but the difference between deepfake images and real images is often subtle and partial. Therefore, a detection model FGDD based on fine-grained features of images was constructed in this paper. To address the shortcomings of using only coarse-grained features, FGDD fully learned the fine-grained features of the sample images by multi-branch, and improved the accuracy of locating sensitive facial regions by introducing a channel attention mechanism and an optimized activation map mask localization strategy. In the activation graph, the multi-level sliding windows were used to extract the highly differentiated subtle features in the samples, and the robustness of the model for fine-grained features was improved by using AugMix data enhancement strategy. The experimental results show that the tested accuracy of FGDD on several datasets outperform the mainstream algorithms, proving the effectiveness of the detection method based on fine-grained features of images.

Key words: deepfake, deepfake detection, fine-grained features, data augment

CLC Number: