  With the development of deep learning processors and accelerators, deep
learning models have been widely deployed on edge devices as part of the
Internet of Things. Edge device models are generally considered as valuable
intellectual properties that are worth for careful protection. Unfortunately,
these models have a great risk of being stolen or illegally copied. The
existing model protections using encryption algorithms are suffered from high
computation overhead which is not practical due to the limited computing
capacity on edge devices. In this work, we propose a light-weight, practical,
and general Edge device model Pro tection method at neuron level, denoted as
EdgePro. Specifically, we select several neurons as authorization neurons and
set their activation values to locking values and scale the neuron outputs as
the "asswords" during training. EdgePro protects the model by ensuring it can
only work correctly when the "passwords" are met, at the cost of encrypting and
storing the information of the "passwords" instead of the whole model.
Extensive experimental results indicate that EdgePro can work well on the task
of protecting on datasets with different modes. The inference time increase of
EdgePro is only 60% of state-of-the-art methods, and the accuracy loss is less
than 1%. Additionally, EdgePro is robust against adaptive attacks including
fine-tuning and pruning, which makes it more practical in real-world
applications. EdgePro is also open sourced to facilitate future research:
https://github.com/Leon022/Edg
