  The surge in popularity of machine learning (ML) has driven significant
investments in training Deep Neural Networks (DNNs). However, these models that
require resource-intensive training are vulnerable to theft and unauthorized
use. This paper addresses this challenge by introducing DNNShield, a novel
approach for DNN protection that integrates seamlessly before training.
DNNShield embeds unique identifiers within the model architecture using
specialized protection layers. These layers enable secure training and
deployment while offering high resilience against various attacks, including
fine-tuning, pruning, and adaptive adversarial attacks. Notably, our approach
achieves this security with minimal performance and computational overhead
(less than 5\% runtime increase). We validate the effectiveness and efficiency
of DNNShield through extensive evaluations across three datasets and four model
architectures. This practical solution empowers developers to protect their
DNNs and intellectual property rights.
