The advancement of Machine Learning (ML) in the last decade has created new business prospects for developers working on ML models. Models that are expensive and time-consuming to design and train can now be outsourced from others to reduce costs using Machine Learning as a service (MLaaS). \textbf{Deep Neural Networks (DNNs)} are particularly expensive to train, therefore many who need a DNN utilize the services of an MLaaS provider. This creates the \textbf{possibility of piracy} of this valuable asset, and the need to prevent piracy to assure a fair market. To address this need, research has been conducted on protecting DNNs using various watermarking techniques. A work by \textit{Li et al.} has proposed null-embedding, a technique that renders the DNN useless if it is subject to a piracy attack. Despite being effective, this method was shown to reduce classification performance when embedding a watermark into the model. This paper suggests modifications to the null-embedding technique that reduce this impact and keep the classification accuracy close to that of a non-watermarked model.