•
Snapshot
◦
Real-time masking others’ face except for my face
◦
Application case: YouTube
•
Abstract
With the growing popularity of online video and streaming platforms, such as YouTube and Twitch, watching videos or live streams has become a common part of our daily life. However, without the proper privacy protection, private lives of both the public and online streamers can be revealed through shared videos and live streaming. It occurs not only due to the lack of privacy knowledge, but also due to the lack of privacy protection tools that allow users to filter sensitive information without much burden.
In this regard, the aim of this project is to present a deep learning-based privacy protection tool which can be used to filter pedestrians’ faces in videos or live streams in real time. Two major issues should be considered when building the tool: (1) streaming or sharing videos should not be transmitted to the cloud for inference to prevent possible privacy abuses, and (2) computation cost of the tool should be low enough to be in real time realm. To overcome these issues, the authors leveraged light weight deep neural networks, such as MobileNetV2 and SSD (Single shot detector), and Google Coral Dev Board, a commercially available development board containing Google Edge TPU coprocessor – a 8-bit fixed point hardware.
•
Development process
•
Model
◦
SSD MobileNet V2 for face detection
▪
Tensorflow object detection API + Face dataset (WIDERFACE)
◦
The model was quantized using Tensorflow lite to deploy on Google Edge TPU (Coral dev board)
