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This work tackles the problem of temporally coherent face anonymization innatural video streams.We propose JaGAN, a two-stage system starting withdetecting and masking out faces with black image patches in all individualframes of the video. The second stage leverages a privacy-preserving VideoGenerative Adversarial Network designed to inpaint the missing image patcheswith artificially generated faces. Our initial experiments reveal that imagebased generative models are not capable of inpainting patches showing temporalcoherent appearance across neighboring video frames. To address this issue weintroduce a newly curated video collection, which is made publicly availablefor the research community along with this paper. We also introduce theIdentity Invariance Score IdI as a means to quantify temporal coherency betweenneighboring frames.

Temporally coherent video anonymization through GAN inpainting