Chaotic Weights: A Novel Approach to Protect Intellectual Property of Deep Neural Networks

Despite the high accuracy achieved by the deep neural network (DNN) technique, there is still a lack of satisfying methodologies to protect the intellectual property (IP) of DNNs, which involves extensive valuable training data, abundant hardware training resources, and fine-tuning skills of experienced experts. Existing solutions based on watermarking cannot prevent malicious/unauthorized users from using well-trained DNNs. This paper proposes chaotic weights (ChaoWs), a novel framework based on the Chaotic Map theory, to protect the IP of DNN providers with very low overhead. Specifically, in order to alleviate the storage overhead and abridge the decryption time, our method makes convolutional or fully connected kernels chaotic by exchanging the weight positions to obtain a satisfying encryption effect, instead of using the conventional idea of encrypting the weight values. Comprehensive experimental evaluations on image classification, semantic segmentation, and name generation demonstrate that ChaoW can effectively protect the IP of DNNs without damaging the inference accuracy, and the impact on the inference speed is negligible.