Dynamic Inconsistency-aware DeepFake Video Detection

Author:

Hu Ziheng1,Xie Hongtao1,Wang YuXin1,Li Jiahong2,Wang Zhongyuan2,Zhang Yongdong1

Affiliation:

1. University of Science and Technology of China

2. Kuaishou Technology

Abstract

The spread of DeepFake videos causes a serious threat to information security, calling for effective detection methods to distinguish them. However, the performance of recent frame-based detection methods become limited due to their ignorance of the inter-frame inconsistency of fake videos. In this paper, we propose a novel Dynamic Inconsistency-aware Network to handle the inconsistent problem, which uses a Cross-Reference module (CRM) to capture both the global and local inter-frame inconsistencies. The CRM contains two parallel branches. The first branch takes faces from adjacent frames as input, and calculates a structure similarity map for a global inconsistency representation. The second branch only focuses on the inter-frame variation of independent critical regions, which captures the local inconsistency. To the best of our knowledge, this is the first work to totally use the inter-frame inconsistency information from the global and local perspectives. Compared with existing methods, our model provides a more accurate and robust detection on FaceForensics++, DFDC-preview and Celeb-DFv2 datasets.

Publisher

International Joint Conferences on Artificial Intelligence Organization

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