Summary of Privacy-preserving Optics For Enhancing Protection in Face De-identification, by Jhon Lopez et al.
Privacy-preserving Optics for Enhancing Protection in Face De-identification
by Jhon Lopez, Carlos Hinojosa, Henry Arguello, Bernard Ghanem
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a hardware-level face de-identification method to address vulnerabilities in software-based solutions. The proposed approach learns an optical encoder along with a regression model to obtain a face heatmap while hiding the face identity from the source image. Additionally, the authors propose an anonymization framework that generates a new face using privacy-preserving images, face heatmaps, and reference faces from public datasets. The method is validated through extensive simulations and hardware experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make faces invisible on cameras, which is important because many people don’t want their faces shared online or stored without consent. Right now, some software tries to solve this problem by hiding faces, but these solutions aren’t perfect. The researchers propose a new method that uses both computer algorithms and special camera hardware to fully hide faces. They test this approach using fake data and real cameras to show it works well. |
Keywords
* Artificial intelligence * Encoder * Regression
