Natural and Effective Obfuscation by Head Inpainting

Abstract

As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted to blacking out or blurring head regions, but they result in poor user experience while being surprisingly ineffective against state of the art person recognizers [16]. In this work, we propose a novel head inpainting obfuscation technique. Generating a realistic head inpainting in social media photos is challenging because subjects appear in diverse activities and head orientations. We thus split the task into two sub-tasks: (1) facial landmark generation from image context (e.g. body pose) for seamless hypothesis of sensible head pose, and (2) facial landmark conditioned head inpainting. We verify that our inpainting method generates realistic person images, while achieving superior obfuscation performance against automatic person recognizers.

Live Face De-Identification in Video

“…In addition, the work of [49] presents results on low-resolution black and white images only, with no pose or gender variation. Figure 5 compares with the recent work of [43,44]. Our method is able to distance the identity in a more subtle way, while introducing less artifact.…”

“…However, the experiments were restricted to a homogeneous dataset, with no apparent expression preservation within the results. In the GAN-based methods of [43,44], face de-identification is employed for the related task of person obfuscation. The work of [43] conditions the output image based on both a blurred version of the input and the extracted facial pose information.…”

“…In the GAN-based methods of [43,44], face de-identification is employed for the related task of person obfuscation. The work of [43] conditions the output image based on both a blurred version of the input and the extracted facial pose information. The follow-up work [44] combines the GAN-based reconstruction with a parametric face generation network.…”

“…(a) (b) (c) Figure 5. (a) Input images from [43,44], (b) our results, (c) those of [43] (row 1) and [44] (rows 2-3).…”

“…As far as we know, this is the first time that a representation from an existing classifier network is used to augment an autoencoder, which enables the feed-forward treatment of new persons, unseen during training. In addition, this is the first work to introduce a new Newton, '05 [32] Gross, '08 [10] Samarzija, '14 [41] Jourabloo, '15 [16] Meden, '17 [31] Wu, '18 [49] Sun'18 [43,44] Our Preserves We compare to all methods that provide reasonable quality images in their manuscript, under conditions that are favorable to previous work (we crop the input images from the pdf files, except for the images received from the authors of [43,44]). † The face is swapped with an average of a few dataset faces.…”