Source-ID-Tracker: Source Face Identity Protection in Face Swapping

Yuzhen Lin, Han Chen, Emanuele Maiorana, Patrizio Campisi, Bin Li

Abstract: Swapping faces with deep learning technology to generate realistic fake videos/images (a.k.a, deepfakes) has drawn great public con-cerns recently. Numerous approaches have been proposed to iden-tify fake contents; however, less work has been dedicated to pro-tecting the source faces in an active way. In this paper, we stand for a legitimate faceswap service provider and present an approach called Source-ID- Tracker (SIDT), which aims to protect the identity of source faces in deepfakes from malicious uses. As a plug-in, the encoder of SIDT implicitly embeds a source face image into a deep-fake image while ensuring the resultant encoded image is visually indistinguishable from the deepfake image. After sharing through social media, the embedded source face and its identity can still be recovered with a decoder. Experimental results show that the pro-posed model achieves a promising performance, in terms of reconstruction quality and attribution inference accuracy, in revealing the hidden source face.