Name
Abstract | ||
|---|---|---|
Face de-identification has become increasingly important as the image sources are explosively growing and easily accessible. The advance of new face recognition techniques also arises people’s concern regarding the privacy leakage. The mainstream pipelines of face de-identification are mostly based on the k-same framework, which bears critiques of low effectiveness and poor visual quality. In this paper, we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN (generative adversarial network) with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input. We evaluate the proposed approach in terms of privacy protection, utility preservation, and structure similarity. Our approach not only outperforms existing face de-identification techniques but also provides a practical framework of adapting GAN with priors of domain knowledge. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11390-019-1898-8 | J. Comput. Sci. Technol. |
Keywords | Field | DocType |
face de-identification, privacy protection, image synthesis, generative adversarial network (GAN) | Facial recognition system,Generative adversarial network,Domain knowledge,De-identification,Computer science,Image synthesis,Artificial intelligence,Prior probability,Machine learning,Distributed computing | Journal |
Volume | Issue | ISSN |
34 | 1 | 1860-4749 |
Citations | PageRank | References |
5 | 0.57 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifan Wu | 1 | 26 | 8.16 |
| Fan Yang | 2 | 196 | 48.38 |
| Yong Xu | 3 | 47 | 12.27 |
| Haibin Ling | 4 | 4531 | 215.76 |