Active intellectual property protection for deep neural networks through stealthy backdoor and users' identities authentication

Recently, the intellectual properties (IP) protection of deep neural networks (DNN) has attracted serious concerns. A number of DNN copyright protection methods have been proposed. However, most of the existing DNN watermarking methods can only verify the ownership of the model after the piracy occurs, which cannot actively prevent the occurrence of the piracy and do not support users' identities management, thus can not satisfy the requirements of commercial DNN copyright management. In addition, the query modification attack which was proposed recently can invalidate most of the existing backdoor-based DNN watermarking methods. In this paper, we propose an active intellectual properties protection technique for DNN models via stealthy backdoor and users' identities authentication. For the first time, we use a set of clean images (as the watermark key samples) to embed an additional class into the DNN for ownership verification, and use the image steganography to embed users' identity information into these watermark key images. Each user will be assigned with a unique identity image for identity authentication and authorization control. Since the backdoor instances are clean images outside the dataset, the backdoor trigger is visually imperceptible and concealed. In addition, we embed the watermark by exploiting an additional class outside the main tasks, which establishes a strong connection for watermark key samples and the corresponding label. As a result, the proposed method is concealed, robust, and can resist common attacks and query modification attack. Experimental results demonstrate that, the proposed method can obtain 100% watermark accuracy and 100% fingerprint authentication success rate on Fashion-MNIST and CIFAR-10 datasets. In addition, the proposed method is demonstrated to be robust against the model fine-tuning attack, model pruning attack, and query modification attack. Compared with three existing DNN watermarking methods, the proposed method has better performance on watermark accuracy and robustness against the query modification attack.