  Machine learning as a service (MLaaS) framework provides intelligent services
or well-trained artificial intelligence (AI) models for local devices. However,
in the process of model transmission and deployment, there are security issues,
i.e. AI model leakage due to the unreliable transmission environments and
illegal abuse at local devices without permission. Although existing works
study the intellectual property (IP) protection of AI models, they mainly focus
on the watermark-based and encryption-based methods and have the following
problems: (i) The watermark-based methods only provide passive verification
afterward rather than active protection. (ii) Encryption-based methods are low
efficiency in computation and low security in key storage. (iii) The existing
methods are not device-bind without the ability to avoid illegal abuse of AI
models. To deal with these problems, we propose a device-bind and
key-storageless hardware AI model IP protection mechanism. First, a physical
unclonable function (PUF) and permute-diffusion encryption-based AI model
protection framework is proposed, including the PUF-based secret key generation
and the geometric-value transformation-based weights encryption. Second, we
design a PUF-based key generation protocol, where delay-based Anderson PUF is
adopted to generate the derive-bind secret key. Besides, convolutional coding
and convolutional interleaving technologies are combined to improve the
stability of PUF-based key generation and reconstruction. Third, a permute and
diffusion-based intelligent model weights encryption/decryption method is
proposed to achieve effective IP protection, where chaos theory is utilized to
convert the PUF-based secret key to encryption/decryption keys. Finally,
experimental evaluation demonstrates the effectiveness of the proposed
intelligent model IP protection mechanism.
