  As a type of valuable intellectual property (IP), deep neural network (DNN)
models have been protected by techniques like watermarking. However, such
passive model protection cannot fully prevent model abuse. In this work, we
propose an active model IP protection scheme, namely NNSplitter, which actively
protects the model by splitting it into two parts: the obfuscated model that
performs poorly due to weight obfuscation, and the model secrets consisting of
the indexes and original values of the obfuscated weights, which can only be
accessed by authorized users with the support of the trusted execution
environment. Experimental results demonstrate the effectiveness of NNSplitter,
e.g., by only modifying 275 out of over 11 million (i.e., 0.002%) weights, the
accuracy of the obfuscated ResNet-18 model on CIFAR-10 can drop to 10%.
Moreover, NNSplitter is stealthy and resilient against norm clipping and
fine-tuning attacks, making it an appealing solution for DNN model protection.
The code is available at: https://github.com/Tongzhou0101/NNSplitter.
