Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12357)


As realistic facial manipulation technologies have achieved remarkable progress, social concerns about potential malicious abuse of these technologies bring out an emerging research topic of face forgery detection. However, it is extremely challenging since recent advances are able to forge faces beyond the perception ability of human eyes, especially in compressed images and videos. We find that mining forgery patterns with the awareness of frequency could be a cure, as frequency provides a complementary viewpoint where either subtle forgery artifacts or compression errors could be well described. To introduce frequency into the face forgery detection, we propose a novel Frequency in Face Forgery Network (F\(^3\)-Net), taking advantages of two different but complementary frequency-aware clues, 1) frequency-aware decomposed image components, and 2) local frequency statistics, to deeply mine the forgery patterns via our two-stream collaborative learning framework. We apply DCT as the applied frequency-domain transformation. Through comprehensive studies, we show that the proposed F\(^3\)-Net significantly outperforms competing state-of-the-art methods on all compression qualities in the challenging FaceForensics++ dataset, especially wins a big lead upon low-quality media.


Face forgery detection Frequency Collaborative learning 



This work is supported by SenseTime Group Limited, in part by key research and development program of Guangdong Province, China, under grant 2019B010154003. The contribution of Yuyang Qian and Guojun Yin are Equal.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.SenseTime ResearchHong KongChina
  2. 2.University of Electronic Science and Technology of ChinaChengduChina
  3. 3.College of SoftwareBeihang UniversityBeijingChina
  4. 4.Northwestern Polytechnical UniversityXi’anChina

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