Wearable cameras are opening up exciting new applications, but will also require new techniques to help people preserve their privacy.
A group of researchers from Indiana University and Olin College used the Caffe deep learning framework and a Tesla K20 GPU to automatically detect private content on monitors that people may not want to be recorded.
As a first step, they trained their model to detect computer monitors and phone screens, which achieved 99.8% near-human accuracy. They then trained a Convolutional Neural Network to classify whether each screen is displaying a privacy-sensitive application such as Facebook, GMail, Mac OS Messages, or other similar applications – and their solution achieved 54.2% accuracy even though many of the screens were not fully visible.
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Deep Learning to Help Preserve Privacy from Wearable Cameras
Mar 07, 2016
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AI-Generated Summary
- Researchers from Indiana University and Olin College trained a model to detect computer monitors and phone screens with 99.8% accuracy using the Caffe deep learning framework and a Tesla K20 GPU.
- A Convolutional Neural Network then classified whether each screen displayed privacy-sensitive applications such as Facebook, GMail, or Mac OS Messages, achieving 54.2% accuracy even with partially visible screens.
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- Read the research paper for detailed methodology and results.
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