A method for protecting a machine learning model from copying is provided. The method includes providing a neural network architecture having an input layer, a plurality of hidden layers, and an output layer. Each of the plurality of hidden layers has a plurality of nodes. A neural network application is provided to run on the neural network architecture. First and second types of activation functions are provided. Activation functions including a combination of the first and second types of activation functions are provided to the plurality of nodes of the plurality of hidden layers. The neural network application is trained with a training set to generate a machine learning model. Using the combination of first and second types of activation functions makes it more difficult for an attacker to copy the machine learning model. Also, the neural network application may be implemented in hardware to prevent easy illegitimate upgrading of the neural network application.