Summary of Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation For Action Recognition, by Filip Ilic et al.
Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition
by Filip Ilic, He Zhao, Thomas Pock, Richard P. Wildes
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 tackles the challenge of ensuring privacy in public imagery action recognition, where global obfuscation methods often hide important contextual information alongside sensitive regions. The current approaches lack interpretability, making it difficult to trust these technologies. To address this issue, the authors propose a novel approach that utilizes human-selected privacy templates, which yields interpretability by design and selectively hides attributes while maintaining temporal consistency. This architecture-agnostic method directly modifies input imagery without requiring retraining, outperforming existing alternatives on three popular datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simpler terms, this research is trying to solve the problem of keeping people’s personal information private when recognizing actions in public pictures. Current methods often hide important details along with sensitive areas, making it hard to trust these technologies. To fix this, scientists are introducing a new way that lets humans choose how much to hide and helps keep the resulting images consistent over time. This method works with different computer systems without needing any special training, and it even performs better than other approaches on three big datasets. |
