  The smartphone and laptop can be unlocked by face or fingerprint recognition,
while neural networks which confront numerous requests every day have little
capability to distinguish between untrustworthy and credible users. It makes
model risky to be traded as a commodity. Existed research either focuses on the
intellectual property rights ownership of the commercialized model, or traces
the source of the leak after pirated models appear. Nevertheless, active
identifying users legitimacy before predicting output has not been considered
yet. In this paper, we propose Model-Lock (M-LOCK) to realize an end-to-end
neural network with local dynamic access control, which is similar to the
automatic locking function of the smartphone to prevent malicious attackers
from obtaining available performance actively when you are away. Three kinds of
model training strategy are essential to achieve the tremendous performance
divergence between certified and suspect input in one neural network. Extensive
experiments based on MNIST, FashionMNIST, CIFAR10, CIFAR100, SVHN and GTSRB
datasets demonstrated the feasibility and effectiveness of the proposed scheme.
