Detailed info
PhiNet-GAN: Bringing real-time face swapping to embedded devices
| Authors | Ancilotto, Alberto; Paissan, Francesco; Farella, Elisabetta, |
| Title | PhiNet-GAN: Bringing real-time face swapping to embedded devices |
| Abstract | Recent years have seen an unprecedented development of deep learning-based techniques for processing live video from CCTV cameras, causing growing privacy concerns. A possible solution is to ensure that a subject’s personal information never leaves the device in which it was collected, thus implementing a Privacy-by-Design (PbD) approach. In live video processing tasks, PbD can be guaranteed through anonymisation techniques, such as face-swapping, performed directly on the end device. This paper, therefore, presents PhiNet-GAN, an extension of the PhiNet family of embedded neural networks applied to generative networks. PhiNet-GAN targets resource-constrained platforms based on low-power microcontrollers. An example is the Kendryte K210, a RISC V dual-core processing unit working at 400MHz on which we tested our network. Overall we achieved a power consumption of less than 300mW, working at more than 15fps with an FID score lower than 150. |
| Conference | 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) |
| Date | 13-17/03/2023 |
| Location | Atlanta, US |
| Year of Publication | 2023 |
| Url | https://zenodo.org/records/10629777 |
| DOI | 10.1109/PerComWorkshops56833.2023.10150292 |
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Funding
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.
