  Trained Deep Neural Network (DNN) models are considered valuable Intellectual
Properties (IP) in several business models. Prevention of IP theft and
unauthorized usage of such DNN models has been raised as of significant concern
by industry. In this paper, we address the problem of preventing unauthorized
usage of DNN models by proposing a generic and lightweight key-based
model-locking scheme, which ensures that a locked model functions correctly
only upon applying the correct secret key. The proposed scheme, known as
Deep-Lock, utilizes S-Boxes with good security properties to encrypt each
parameter of a trained DNN model with secret keys generated from a master key
via a key scheduling algorithm. The resulting dense network of encrypted
weights is found robust against model fine-tuning attacks. Finally, Deep-Lock
does not require any intervention in the structure and training of the DNN
models, making it applicable for all existing software and hardware
implementations of DNN.
