  Deep neural networks are playing an important role in many real-life
applications. After being trained with abundant data and computing resources, a
deep neural network model providing service is endowed with economic value. An
important prerequisite in commercializing and protecting deep neural networks
is the reliable identification of their genuine author. To meet this goal,
watermarking schemes that embed the author's identity information into the
networks have been proposed. However, current schemes can hardly meet all the
necessary requirements for securely proving the authorship and mostly focus on
models for classification. To explicitly meet the formal definitions of the
security requirements and increase the applicability of deep neural network
watermarking schemes, we propose a new framework based on multi-task learning.
By treating the watermark embedding as an extra task, most of the security
requirements are explicitly formulated and met with well-designed regularizers,
the rest is guaranteed by using components from cryptography. Moreover, a
decentralized verification protocol is proposed to standardize the ownership
verification. The experiment results show that the proposed scheme is flexible,
secure, and robust, hence a promising candidate in deep learning model
protection.
