An Analytical Framework for Trusted Machine Learning and Computer Vision Running With Blockchain

Tao Wang, Maggie Du, Xinmin Wu, Taiping He; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 6-7

Machine learning algorithms often use data from databases that are mutable; therefore, the data and the results of machine learning cannot be fully trusted. Also, the learning process is often difficult to automate. A unified analytical framework for trusted machine learning has been presented in the literature to address both issues. It proposed building a trusted machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts on blockchain are used to automate the machine learning process. However, in such a blockchain framework, data efficiency is a big concern, because it is very expensive to store a large amount of data on blockchain. On the other hand, machine learning-based computer vision systems often rely on a lot of data. Therefore, to fully leverage a blockchain-based machine learning framework for computer vision systems, data efficiency issues must be addressed. This paper investigates how to enhance data efficiency in such a framework to bring computer vision systems to the edge. It presents a three-step approach. First, a lightweight machine learning model is trained on the server layer. Second, the trained model is saved in a special binary data format for data efficiency. Finally, the streaming layer takes these binary data as input and scores incoming new data in an online fashion. Real-time semantic segmentation for autonomous driving is used as an example to demonstrate how this approach works. This paper makes the following contributions. First, it improves the analytical framework for fair and trusted computer vision systems based on blockchain. Second, the real-time semantic segmentation example shows how data-efficient learning for computer vision can be performed on the edge.